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| author | ShelbySolomonDarnell | 2024-10-17 12:24:26 +0300 |
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| committer | ShelbySolomonDarnell | 2024-10-17 12:24:26 +0300 |
| commit | 00cba4b9a1e88891f1f96a1199320092c1962343 (patch) | |
| tree | 270fd06daa18b2fc5687ee72d912cad771354bb0 /gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files | |
| parent | e0b2b0e55049b89805f73f291df1e28fa05487fe (diff) | |
| download | gn-ai-master.tar.gz | |
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diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_1 new file mode 100644 index 0000000..8bc7dfe --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf", + "2001 - The genetics of aging.pdf", + "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2017 - Genome-wide transcriptomics of aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "0104338d-cc9c-538f-be29-8343a64da37d", + "4ea8424f-1cd8-569c-a1df-3f0f54206e70", + "bcb3c620-b960-5af6-95ea-13215c31672e", + "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "210aa417-372c-5bf6-b961-e281a1817458", + "34223e0e-590c-5f26-b120-b7250cd91b99", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "c7d6d597-a9c7-5db2-888d-5f9297f0af47", + "517379dd-d351-5e9a-8e78-72e543bb2945" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "0dc45abe-ab02-5b07-9916-7093b53323c0", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "1a2a3737-b0a6-58b9-908f-50753241a309", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-AIFgRqvOB8PnpNpKMnpdr80oxf2MI", + "3117c019-7311-53ae-8ab1-927ca822c709", + "a9434032-4a9d-54f8-a7a6-16110d1b3118", + "a0672677-71ad-5603-8427-a0648eec407f", + "c1b5a31a-066d-571b-af1f-db746d9d17f6", + "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "2d0a20b8-4196-5451-9d99-282f82234464", + "8bcb7ae0-ac45-5b4c-8a4b-626564e8ec11", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "d811de8c-b666-5bb5-b0eb-a9b17fa16a8e", + "081e12f9-359c-5a2c-b740-714d637367d3" + ], + "contexts": [ + "It is undisputed that genetic factors influence aging. In a remarkable", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "Clinical Genetics and Genomics of Aging", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-", + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "effect genetic variants on human longevity. Aging 2, 612620. Yu, C.E., Seltman, H., Peskind, E.R., Galloway, N., Zhou, P.X., Rosenthal, E., Wijsman, E.M., Tsuang, D.W., Devlin, B., Schellenberg, G.D., 2007. Comprehensive analysis of APOE and selected proximate markers for late-onset Alzheimers disease: patterns of linkage disequilibrium and disease/marker association. Genomics", + "factors shape a complex scenario for which clear answers of the regulation of longevity have been dicult to distill. With the discovery of genetic factors underlying aging in experimental laboratory models, forays into the genetic regulation of these properties have rapidly expanded, uncovering conserved mechanisms across diverse metazoa that inuence expression of aging phenotypes and lifespan. Yet, the story gets muddled in that these factors are often", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "tion for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study. Research from model systems hascharacterized a range of physiological and molecular phenotypes associated with aging. These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attri- tion; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated", + "143 The molecular bases of ageing are multi factorial, but there are nine distinctive features related to this process, which include genomic instability, telomere shorten- ing, de-regulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered cellular senescence, loss of proteostasis and a change in the patterns of epigenetic modifications [4, 5]. Epigenetics andAgeing Epigenetics is considered as a dynamic interface between the genome and the envi-" + ], + "answer": "The main genetic factors that influence aging include: 1. **Genomic Instability**: Aging is associated with the accumulation of DNA damage and the dysregulation of repair mechanisms, leading to genomic instability [9]. 2. **Telomere Attrition**: The shortening of telomeres, which are protective caps at the ends of chromosomes, is a significant factor in aging [9], [10]. 3. **Epigenetic Alterations**: Changes in epigenetic marks, which regulate gene expression without altering the DNA sequence, play a crucial role in aging [2], [4], [9], [10]. 4. **Deregulated Nutrient Sensing**: The pathways that sense and respond to nutrients become deregulated with age, affecting longevity [10]. 5. **Mitochondrial Dysfunction**: Mitochondria, the energy-producing organelles in cells, become less efficient with age, contributing to the aging process [10]. 6. **Cellular Senescence**: The process by which cells lose the ability to divide and function properly is a hallmark of aging [9], [10]. 7. **Loss of Proteostasis**: The ability of cells to maintain protein homeostasis declines with age, leading to the accumulation of damaged proteins [9], [10]. 8. **Stem Cell Exhaustion**: The decline in the regenerative capacity of stem cells contributes to aging [10]. These factors collectively shape the complex genetic landscape of aging, influencing the expression of aging phenotypes and lifespan [7].", + "question": "What are the main genetic factors that influence aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_10 new file mode 100644 index 0000000..d9ddb50 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1993 - Genomic Damage and Its Repair.pdf", + "2007 - Trends in oxidative aging theories.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "2002 - Large genome rearrangements as a primary cause of aging.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2016 - Menopause Genome stability as new paradigm.pdf" + ], + "extraction_id": [ + "64063108-0ff2-54e5-9801-bc1c49cbdee4", + "752c6f1a-0c4d-5419-86cd-687d2aed7817", + "ead14808-bfb7-5e32-9830-28efaae71151", + "d620ea24-4422-5636-86f5-0943371a4a18", + "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "8f1a0875-8179-5d45-abc0-bbd4c9ac8da5", + "17b26647-4659-5f2d-a9b0-7c122d4b5d1a", + "72beba0d-8c77-5aa9-82ac-ddf6a19355ac", + "31088092-778f-59e0-a9de-5ec25c241aab", + "0855231d-cb95-540c-a3dd-c93729efb34c" + ], + "document_id": [ + "d049f302-a130-5ee4-a1b5-5091605d5173", + "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "8a8926dc-2360-5a54-b586-8acc34e51c32", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "564dead1-2737-572f-860c-f00de4d0395e" + ], + "id": [ + "chatcmpl-AIFhaX8SGHyXXi1vHCPCMEW3CahGu", + "a9f7eda5-1b64-507e-95dd-07c81f2d603b", + "882149e3-8186-5577-a2a7-79f2659ff9b4", + "da4e59b7-d5b6-5992-9607-f6697c8f5276", + "4841d806-98b4-513e-94a2-714df6c896f5", + "fc10c968-3108-5c4b-a49c-cb0feabd18c5", + "eb8b89de-422a-5e9e-9ac8-60af4cd718c2", + "34e6b3c4-63bf-5198-ab09-2a7200a7c19a", + "beed04cc-28c7-5dc7-b334-51226a217439", + "badf3a36-1f99-58aa-b80c-725eccf4e8f3", + "c35d1f43-c3bd-5cac-ae4d-937be35f1121" + ], + "contexts": [ + "logical phenomena is often facilitated by the study of genetic mutants, and, in the case of humans, genetic disorders. Accordingly, a search was made, over the years, for genetic disorders characterized by premature aging. If DNA dam- age and repair has anything to do with aging it should be evidenced in such individuals. Martin (1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. About 21 feahares are considered as markers for", + "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA 45:3545; 1959. [316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of aging. Mech. Ageing Dev. 123:907915; 2002. [317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res. 447:117135; 2000. [318] Martin, G. M. Genetic syndromes in Man with potential relevance to the pathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "19 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. 2017;94:3440. 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. DNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777. 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 33, 1829. 144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and related RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. 145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? Ageing Res. Rev. 33, 3651. 146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542. [2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and Rose, M.R. (1996) Metabolic aspects of the trade-off between fecundity and longevity in Drosophila melanogaster ,Physiol. Zool. 69, 11751195. [3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA mutations and deletions were the driving force behind the observed accelerated aging phenotypes(18). THE LINK BETWEEN NUCLEAR GENOME INTEGRITY AND PREMATURE AGING The notion that the majority of currently identied progeria syndromes originate from defects in genome maintenance highlights the importance of the condition of DNA in the process of", + "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based premature aging,NatMed. 2005;11:780 785. 13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when repairgetsold,NatMed. 2005;11:718 719. 14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De SandreGiovannoli A,LvyN.HGPSandrelatedpremature aging disorders: Fromgenomicidentification tothefirsttherapeutic approaches, MechAgeingDev.2008;129:449 459. 15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "Nature Genetics | Volume 55 | February 2023 | 268279 278 Article https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Hum. Genet. 105, 237257 (2019). 22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry 50, 23212329 (2011).", + "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al., Premature aging in mice decient in DNA repair and transcription, Science 296 (2002) 12761279. [37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A. Reijo Pera, Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities, Hum. Genet. 131 (2012) 17091724." + ], + "answer": "Yes, there are known genetic mutations that cause premature aging. Specifically, mutations in the LMNA gene are associated with Hutchinson-Gilford Progeria Syndrome, a disease characterized by premature aging [4]. Additionally, mutations in the RECQL4 gene are linked to Rothmund-Thomson syndrome, which also involves premature aging [4]. These genetic disorders highlight the connection between genome integrity and premature aging [7].", + "question": "Are there any known genetic mutations that cause premature aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_11 new file mode 100644 index 0000000..5488b92 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Mitonuclear genomics and aging.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2001 - The genetics of aging.pdf", + "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "2009 - Experimental_Evolution.pdf", + "2015 - Great Is Their Sin.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "2019 - Genomics of 1 million parent lifespans.pdf", + "2018 - Sex Differences in Aging Genomic Instability.pdf" + ], + "extraction_id": [ + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "5aa7f5b9-df70-54ec-a95c-dcaefa3b617f", + "c9d59e72-f068-58da-be7a-71b2f51a23f3", + "44c57701-0d0e-5ef8-afa1-ea3a6c4742d6", + "ead14808-bfb7-5e32-9830-28efaae71151", + "dd4a6239-2e79-5b99-89ef-3e4939b87805", + "ff0adc7c-70ff-5b14-ba7d-a9dda60fac80", + "0104338d-cc9c-538f-be29-8343a64da37d" + ], + "document_id": [ + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "34821353-1b74-5ee2-ac39-66dd46f145bf", + "e5ae9710-3049-5327-82e4-e6626eb670c2", + "62b635c3-040e-512a-b016-6ef295308a1e", + "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "f68b939c-847b-5eac-8926-24713ae43478", + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f" + ], + "id": [ + "chatcmpl-AIFhez5FFXsDDkyj8CmiEuE5k6YSr", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "1c4286b6-ede2-568b-9c18-b1e99ede17a6", + "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "f7120061-9773-5f74-9760-5442d49fbaae", + "d0e74ffd-034d-5e0e-86b6-4cf0de57d774", + "217c3592-1622-503f-a140-fd1452083301", + "b3e21ac9-8df8-5119-a769-a9da82db78da", + "fd811aec-6e33-5078-83d5-b68bd59b5a61", + "de7c30f6-cce9-563d-83f4-809f2aab781b", + "a9434032-4a9d-54f8-a7a6-16110d1b3118" + ], + "contexts": [ + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "Recent developments on the genetics of aging can be seen as several streams of effort. In general, humans show a relatively modest ( <50%) heritability of", + "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "genetics of aging I. What is aging? Frontiers in Genetics. doi:10.3389/fgene.2012.00134. r ose, Michael r ., Anthony D. Long, Laurence D. Mueller, Cristina L. r izza, Kennedy C. Matsagas, LeeF. Greer, and Bryant villeponteau. 2009. e volutionary nutrigenomics. In The future of aging, eds. G. M. Fahy, M. D. West, L. S. Coles, and S. B. h arris. Berlin: Springer. r ushton, J. p hillippe. 1995. Race, evolution, and behavior: A life history approach. New Brunswick, NJ: Transaction p ublishers.", + "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen. 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, etal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:7029. 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. 2017;70:17789.", + "eries that have inspired thousands of researchers across the world to study aging, and we acknowledge the wider significance of the creation of a field that has the potential to transform human health. Genetics Aging is influenced by genetic factors. It may be surprising to know that as recently as the 1970s and 1980s, the concept of modulating Downloaded from https://academic.oup.com/biomedgerontology/article/76/7/e85/6145792 by guest on 15 October 2023", + "discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. DOI: https://doi.org/10.7554/eLife.39856.002", + "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link- ing environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity and aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050" + ], + "answer": "Recent discoveries in the genetics of aging include the identification of a number of genes capable of altering the aging process significantly in animal models and even in some humans [2]. Additionally, recent efforts have focused on isolating aging mutants through mutagenesis experiments to determine the mechanistic basis for unusual life spans, leading to the discovery of genes that can either enhance or reduce life span [4]. These findings contribute to a growing understanding of the genetic factors influencing aging and longevity.", + "question": "What recent discoveries have been made about the genetics of aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_12 new file mode 100644 index 0000000..8332dac --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2017 - Epigenetic aging signatures in mice livers.pdf", + "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "2010 - Higher-order Genome Organization.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2016 - Epigenetic drift in the aging genome a ten-year.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf" + ], + "extraction_id": [ + "8db25d5e-25bd-5873-a53d-3815badbfd32", + "8e77571a-60ea-5e53-877d-08169e86d553", + "6c8b334f-2e14-5099-85fe-93f9ed6046ad", + "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "42a32702-d380-56e7-8309-6ab1efbcd479", + "eb735cbe-b7f5-56cb-8cd6-1f2fb2b990a4", + "2f6d20f0-addc-51e8-979d-1aac7ac26694", + "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "0a77e94f-85de-5ab3-b23f-129b08839502", + "0e274732-b0df-53b8-999b-30b798af92e2" + ], + "document_id": [ + "70945353-4808-539a-80f9-5632c27913e5", + "b20b11a6-1490-51b8-9218-c441a2e65ba7", + "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "52f09ef3-4e4c-538f-909c-d28eb72d91f3", + "70945353-4808-539a-80f9-5632c27913e5", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "bde26feb-f423-51b0-89ec-6f079bfc8b17" + ], + "id": [ + "chatcmpl-AIFhiQpAn2q80Ivtr6ZFWK3Q6aNNm", + "e97baf15-d14f-563f-875f-a2a6bd314ad4", + "38dc7635-499d-52ba-a35f-f57ba8f8df22", + "975250c9-7b4b-5ce5-b907-606ca6c70641", + "7fad6aa1-d4d9-585c-bdbd-394a9552ec0e", + "b43c1348-b982-59c2-9685-af7bb9fd0c4a", + "33f6a665-bb01-5c9f-9325-0f9acf312b54", + "0aede05b-f0dd-595a-a11d-acac0970d25d", + "5e3a0748-9dc0-55b1-ac4d-d8b2291fa297", + "c35ad17b-fe97-5ce5-bae1-59fd08201a7b", + "dea115e3-3d9b-5d08-a604-ab227fcd1b71" + ], + "contexts": [ + "Figure 1. Epigenetics of aging and aging-relate d diseases. During aging, various ep igenetic alterations occur including accumulation of histone variants, change s in chromatin accessibility mediated by chromatin remodeling complexes, loss of histones and heterochroma tin, imbalance of activating /repressing histone modifications and aberrant expres- sion/activity of miRNAs. These deregulations can affect transcrip tion and, subsequently, transl ation, as well as the stabi-", + "ment of 5 years corresponded to a 21% increased risk of mortality overall [7]. Thus, predictions of epigenetic agemay be an indication of an individual s biological state of aging. Beyond these examples of advanced epigenetic aging, a complementary but unanswered question is whether epigenetic clocks can also be slowed. Epigenetic aging studies in humans have not thus far been well suited to address questions of slowed aging, given the lack of well-documented interventions that enhance health or", + "al., 2005 ). The epigenetic changes that accumulated with age had a dramatic effect on gene expression, thus the authors propos e that a so-called epigenetic drift accompanies the aging process. Epigenetic modifications can result in the cumulative loss of gene regulation over time, ultimately impairing cellular and tissue function. Further, recent data sugge st that epigenetic disruption of tissue specific stem and progenitor cells may play a role in cancer development (Feinberg et al., 2006 ). The", + "epigenetic changes during aging are currentlyunknown (Fig. 3). It has been suggested thatthe epigenetic alterations are largely triggered by DNA damage (reviewed in Oberdoerffer and Sinclair 2007). In this scenario, randomlyoccurring DNA damage leads to chromatin remodeling and to redistribution of chromatin modiers within the genome with modiersbeing recruited away from their normal sites so that they can engage in the repair of the", + "Epigenetic Dysregulation with Age", + "Epigenetic Dysregulation with Age", + "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing attention. The potential reversibility of these epigenetic changes that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases. 8 This is especially important given the remarkable plasticityof aging. 9,10In the literature, age-associated epigenetic alter- ations have been identified by epigenome-wide association", + "in gene transcription and, as a consequence, translation as well as the stabilization or degradation of molecular factors. While mechanisms underlying aging-related pathologies remain to be elucidated in detail, various studies demonstrate an epigenetic component. In fact, the aforementioned epigenetic modications were shown to play essential roles in diseases including inammation, cancer, osteoporosis, neurodegenerative diseases, and diabetes.", + "PLoS Biology | www.plosbiology.org August 2007 | Volume 5 | Issue 8 | e201 1759 Epigenetic Dysregulation with Age", + "and increased expression of proaging genes such as the cell-cycle inhibitor p16, which drives cell senescence. Additional consequences of epigenetic dys-regulation include increased transcriptional noise and decreased coordination of gene expression that contributes to organismal aging. Cell148, January 20, 2012 2012 Elsevier Inc. 53" + ], + "answer": "Epigenetic changes affect aging through several mechanisms: 1. **Alterations in Chromatin Structure**: During aging, there are various epigenetic alterations such as the accumulation of histone variants, changes in chromatin accessibility, loss of histones and heterochromatin, and imbalances in activating/repressing histone modifications. These changes can affect transcription and translation processes, impacting cellular function [1]. 2. **Epigenetic Drift**: As individuals age, epigenetic changes accumulate, leading to a phenomenon known as epigenetic drift. This drift results in the cumulative loss of gene regulation over time, impairing cellular and tissue function. It is suggested that this disruption may also play a role in the development of age-related diseases, such as cancer [3]. 3. **DNA Damage and Chromatin Remodeling**: It is suggested that epigenetic alterations during aging are largely triggered by DNA damage. This damage leads to chromatin remodeling and redistribution of chromatin modifiers, which are recruited away from their normal sites to engage in DNA repair [4]. 4. **Increased Expression of Pro-aging Genes**: Epigenetic dysregulation can lead to increased expression of pro-aging genes, such as the cell-cycle inhibitor p16, which drives cell senescence. This contributes to increased transcriptional noise and decreased coordination of gene expression, further contributing to organismal aging [10]. 5. **Potential for Reversibility**: Despite these changes, there is potential reversibility in epigenetic modifications, offering opportunities to alter the trajectory of age-related diseases. This highlights the plasticity of aging and the potential for interventions that could slow down the aging process [7]. Overall, epigenetic changes play a crucial role in the aging process by affecting gene expression, cellular function, and the development of age-related diseases.", + "question": "How do epigenetic changes affect aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_13 new file mode 100644 index 0000000..6b99b03 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Genomics of human longevity.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2013 - Genome Instability and Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "2019 - A meta-analysis of genome-wide association.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf" + ], + "extraction_id": [ + "7c183ae5-f10e-5f0c-962e-32135887b3bd", + "5cc56e3b-53ab-5299-814d-014e2ed31d2f", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "3091bce3-8eb6-593d-8a92-ee3570e8e9a9", + "68deea31-59de-5665-9c97-df57d72d0b52", + "7555b8ec-cf4e-54a4-b654-6ae7e63d150c", + "210aa417-372c-5bf6-b961-e281a1817458", + "68c41fe5-4413-5cfc-846b-a0097f994bcd", + "bdfc934a-d31b-57e4-9a78-15c719049c4f", + "5cc56e3b-53ab-5299-814d-014e2ed31d2f" + ], + "document_id": [ + "2e038219-fdaa-506f-9cd3-51379054130e", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "71e08916-8cc8-5d96-8c06-4461b972b54d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "9d36fc35-9708-5d1a-9514-9ce3469d7591", + "62b635c3-040e-512a-b016-6ef295308a1e", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b" + ], + "id": [ + "chatcmpl-AIFhpW2QcT6L6LqU3pI7kcz7hsxkv", + "77c88648-7807-5606-8793-4389378a82fd", + "9c463b71-be3a-5f01-bc6f-d1d29b7a162f", + "2f98af09-5895-545a-b36f-c05b70beee07", + "c6e1f317-e421-5f6b-ab4e-034f1aa94ba1", + "34dfec26-9828-56c8-be82-69eb114fa9e3", + "6dd65017-bb91-5a1a-9d85-c1c1cfcd5780", + "160acccd-d5c5-5e54-8f88-ada1d413e91b", + "aceb74e0-8b79-587f-9dd0-e260eeb90ab5", + "049ee89e-2f05-595b-9112-725976cb4ab3", + "f6636c31-1105-5ea2-9b3b-ae8b21e08bee" + ], + "contexts": [ + "27 Willcox, B. J. et al. 2008 FOXO3A genotype is strongly associated with human longevity. Proc. Natl Acad. Sci. USA 105, 13 98713 992. ( doi:10.1073/ pnas.0801030105 ) 28 Flachsbart, F., Caliebe, A., Kleindorp, R., Blanche, H., von Eller-Eberstein, H., Nikolaus, S., Schreiber, S. & Nebela, A. 2009 Association of FOXO3A variationwith human longevity conrmed in GermanGenomics of human longevity P . E. Slagboom et al. 41", + "3. Willcox BJ, Donlon TA, He Q et al (2008) FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci USA 105(37):1398713992. doi: 10.1073/pnas.0801030105 4. Anselmi CV, Malovini A, Roncarati R et al (2009) Association of the FOXO3A locus with extreme longevity in a southern Italian centenarian study. Rejuvenation Res 12(2):95104. doi: 10.1089/ rej.2008.0827 5. Flachsbart F, Caliebe A, Kleindorp R et al (2009) Association of FOXO3A variation with human longevity conrmed in German", + "are, in fact, part of the same insulin/IGF1/GH pathway(Fig. 1) that modulates lifespan across organisms (Ke-nyon, 2010). A strong association between FOXO3 and human longevity has been reported (Willcox et al., 2008)and subsequently validated in other populations (forreview, see Kenyon, 2010). FOXO3 was also associatedAGING GENES AS TARGETS FOR DRUG DISCOVERY 95", + "Biogerontology 11:28797 117. Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, et al. 2008. FOXO3A genotype is strongly associated with human longevity. Proc. Natl. Acad. Sci. USA 105:1398792 118. Soerensen M, Dato S, Christensen K, McGue M, Stevnsner T, et al. 2010. Replication of an association of variation in the FOXO3A gene with human longevity using both case-control and longitudinal data. Aging Cell 9:101017 119. Mardis ER. 2011. A decades perspective on DNA sequencing technology. Nature 470:198203", + "FOXO3 locus is associated with extreme longevity in humans (centenarians) [2, 58, 59]. NRF/SKN-1 activates the expression of genes involved in protecting the cell in response to ROS, toxins, and metabolic changes through mTOR and insulin/IGF signaling, and it is also dysregulated later in life [60, 61]. Increasing the levels of L. Garca-Velzquez and C. Arias", + "A. 2003;100:406671. https://doi.org/10.1073/pnas.2628028100. 24. van den Akker EB, Deelen J, Slagboom PE, Beekman M. Exome and whole genome sequencing in aging and longevity. Adv Exp Med Biol. 2015;847:12739. https://doi. org/10.1007/978-1-4939-2404-2_6. 25. Flachsbart F, etal. Association of FOXO3A variation with human longevity confirmed in German centenarians. Proc Natl Acad Sci U S A. 2009;106:27005. https://doi.org/10.1073/ pnas.0809594106. A. Garca-Venzor and E. A. Mandujano-Tinoco", + "X.L., 2009. Genetic association of FOXO1A and FOXO3A with longevity trait in Han Chinese populations. Hum. Mol. Genet. 18, 48974904. Lunetta, K.L., DAgostino Sr., R.B., Karasik, D., Benjamin, E.J., Guo, C.Y., Govindaraju, R., Kiel, D.P., Kelly-Hayes, M., Massaro, J.M., Pencina, M.J., Seshadri, S., Murabito, J.M., 2007. Genetic correlates of longevity and selected age-related phenotypes:", + "theFOXO3 locus is not surprising, since this locus was previously reported in the longevity GWA study from the CHARGE con- sortium 7, from which many cohorts are included in these meta- analyses. So far, three functional longevity-associated variants have been identi ed at the FOXO3 locus (rs2802292, rs12206094, and rs4946935). For all of them, an allele-speci c response to cellular stress was observed. Consistently, the longevity-associated alleles of all three variants were shown to induce FOXO3", + "exceptional longevity with no significant genetic contribution. Interestingly, the authors found that FOXO3A, a longevity allele, may not be related to healthy aging phenotype [29]. Aging is a complex process usually accompanied by the onset of different dis- eases like neurodegenerative disorders (Alzheimers disease and Parkinsons dis- ease), cardiovascular illnesses, and cancer. The study of the genetic basis of these aging-related diseases is another approach in the study of the genomic basis of", + "centenarians. Proc Natl Acad Sci USA 106(8):27002705. doi: 10. 1073/pnas.0809594106 6. Li Y, Wang WJ, Cao H et al (2009) Genetic association of FOXO1A and FOXO3A with longevity trait in Han Chinese populations. Hum Mol Genet 18(24):48974904. doi: 10.1093/ hmg/ddp459 7. Soerensen M, Dato S, Christensen K et al (2010) Replication of an association of variation in the FOXO3A gene with human longevity using both case-control and longitudinal data. AgingCell 9(6):10101017. doi: 10.1111/j.1474-9726.2010.00627.x" + ], + "answer": "The gene FOXO3 plays a significant role in human longevity. Multiple studies have shown a strong association between variations in the FOXO3 gene and increased lifespan. For instance, the FOXO3A genotype has been strongly linked with human longevity, as demonstrated in studies by Willcox et al. (2008) and confirmed in various populations, including German and Southern Italian centenarians [1], [2], [3]. The FOXO3 locus is associated with extreme longevity in humans, particularly among centenarians [5]. Additionally, specific variants at the FOXO3 locus have been identified that respond to cellular stress, which may contribute to their role in promoting longevity [8].", + "question": "What is the role of the gene FOXO3 in longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_14 new file mode 100644 index 0000000..97bfeb6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2009 - The role of epigenetics in aging and age-related diseases.pdf", + "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2015 - Age and sun exposure-related widespread.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf", + "2018 - Genomics New Light on Alzheimer?s.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf" + ], + "extraction_id": [ + "68b12e10-d643-570a-aa63-eda62de81928", + "53f4a434-3d4b-5ae5-a788-9b32c94fc0ab", + "5a231e39-0927-56e5-970c-e86d69490a69", + "677eccb2-c51a-59dc-9690-7d02c51fbb7a", + "68b12e10-d643-570a-aa63-eda62de81928", + "aa62f800-8e83-5033-889a-64ff3f453fca", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "bcb3c620-b960-5af6-95ea-13215c31672e", + "05bcb479-ca17-57eb-9674-1c2fecb5726c", + "df213743-7428-59be-ba19-2563f8ce5c70" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "62b635c3-040e-512a-b016-6ef295308a1e", + "f7b452fc-0115-5582-b0c0-c2829f090e9d", + "ed31486c-a651-5894-bd96-21fbd78f2646", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "acf06062-9ca8-50be-a543-ef3b34ad6ad3", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "940593d2-04c3-59b9-a5bf-976febbc6f71", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" + ], + "id": [ + "chatcmpl-AIFhuoRML5l0E69TztcoQUZAgCOF2", + "a4773f1a-f2d3-5950-a81e-d22357e97a0f", + "3d657599-d2c8-518d-aee3-46c0643a88ec", + "49127379-fac4-525a-bf90-5c3bae66860a", + "7ce9af40-0bf8-58e1-ad7c-cd55ba0a7cf8", + "3f37774f-e56b-5350-93e8-371948bf3e23", + "3466f905-760d-5d0b-a3e1-b39f506e6289", + "3c369292-4b9c-5156-a80f-4b3301026f30", + "c1b5a31a-066d-571b-af1f-db746d9d17f6", + "90f9e09f-f339-5d59-ae24-fcbdd2ca6ceb", + "c44c36ad-fcca-540a-a4f3-3965e48e3948" + ], + "contexts": [ + "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "Several studies show the influence of the environment on the ageing process [24]. Environmental factors may affect homeostasis and lead to the development of dis- eases, thus affecting the quality of life in older age [25]. They also produce cellular damage, which causes an accelerated shortening of the telomeres at the genetic level, accompanied by changes in DNA methylation, acetylation or deacetylation of histones, among others. Altogether, these changes induce an aberrant gene", + "changes are generated during the aging process. For a long time it has been believed that epigenetic modications occurring during aging may depend on environmental factors. This idea is attractive because, if true, epigenetics could provide a link between the environment, disease and aging. It also opens the possibility of targeted intervention aimed, for example, at improving healthspan or healthy aging. Thus, the rst question is whether specic environmental factors can directly induce specic epigenetic", + "In addition, environmental factors influence the organism s ability to withstand the increase in entropy with aging: for example, caloric restriction and smoking can exert opposite effects on the rate ofaging (Colman et al. 2009 ; Fraser and Shavlik 2001 ). Both protective alleles and a benevolent environment contribute to excess physiological capacity, which in turn indirectly determines an individual s healthy life span and longevity (Martin et al. 2007 ). The well-", + "to humans through ge-netic manipulations for numerous legal, ethical, andtechnical reasons. If we could understand how the envi-ronment modulates these aging-related genes, we mightbe able to create antiaging therapies applicable to hu-mans, potentially through diet, lifestyle, and even phar-macological interventions. Therefore, understanding ge-nome-environment interactions in the context of agingcan be a powerful approach to identify attractive targetsfor drug design.", + "ing human life span have been identified [2,3]. At the same time, there is a growing realization that environ- mental factors are major contributors to aging and age- associated illness. Epigenetics is the study of chemical modifications of the genome, heritable by cell progeny, and it has been an attractive target for studies of aging and environmentally influenced disease. Several groups have shown differences in DNA methylation - a covalent", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91", + "standing the cause and mechanisms of aging is imperative in assisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin- ation of genetic and environmental factors. Previous twin stud- ies have shown that the genetic contribution to general human longevity is about 2030% [ 4,5], whereas environmental factors in human aging and longevity still account for the largest effect. Epigenetic factors influence the regulation of gene expres-", + "known to affect the function of epigenetic regulators, this may be an example of how aging interacts with our genome to inuence AD development.", + "consequently the incidence of age-related diseasessuch as heart disease, cancer, and neurodegenerativediseases, is projected to increase considerably in thecoming decades. Findings from model organisms haverevealed that aging is a surprisingly plastic processthat can be manipulated by both genetic and environ-mental factors. Here we review a broad range of find-ings in model organisms, from environmental to ge-netic manipulations of aging, with a focus on thosewith underlying gene-environment interactions" + ], + "answer": "The environment interacts with genes to influence aging through several mechanisms: 1. **Signaling Pathways and Gene Expression**: Environmental factors can trigger signaling pathways and modulate gene expression, impacting aging. For example, certain genes have varying effects on lifespan depending on environmental factors like diet [1]. 2. **Epigenetic Modifications**: Environmental factors can lead to changes in DNA methylation, acetylation, or deacetylation of histones, which are epigenetic modifications that influence gene expression. These changes can result in cellular damage and accelerated aging, such as the shortening of telomeres [2], [6]. 3. **Epigenetics as a Link**: Epigenetic modifications provide a potential link between the environment, disease, and aging. This suggests that specific environmental factors might directly induce specific epigenetic changes, which could be targeted for interventions aimed at improving healthspan or promoting healthy aging [3]. 4. **Physiological Capacity and Longevity**: Environmental factors, along with protective genetic alleles, contribute to an individual's physiological capacity, indirectly determining healthy lifespan and longevity. For instance, caloric restriction and smoking have opposite effects on the rate of aging [4]. 5. **Plasticity of Aging**: Aging is a plastic process that can be manipulated by both genetic and environmental factors. Understanding these interactions can help identify targets for anti-aging therapies, potentially through diet, lifestyle, and pharmacological interventions [5], [10]. Overall, the interaction between the environment and genes is complex and involves multiple pathways and mechanisms that collectively influence the aging process.", + "question": "How does the environment interact with genes to influence aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_15 new file mode 100644 index 0000000..e458f6f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Nuclear Genomic Instability.pdf", + "2020 - A multidimensional systems biology.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2007 - Two faces of p53 aging and tumor suppression.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2020 - A multidimensional systems biology.pdf" + ], + "extraction_id": [ + "4b00515d-e599-5ce1-84e3-012d7efe1a30", + "95744ef5-34b9-5540-a5e5-01fd580539e6", + "1635dbe1-1dcb-5213-9446-74129d50c5f8", + "6a2a94de-cfc0-50eb-b50e-bf3a0f813c78", + "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "4a95fed4-61db-58e9-96d7-3a9dcf87ef7f", + "10f1fcbd-35a6-507d-880f-1f3f303737ea", + "029ae7be-b0ab-55f8-84a2-5a74681e454d", + "102fcfb3-b333-5b67-ab94-08033f04ba5c", + "fe4ec57e-6ae7-59c4-b8fa-da73fe77ce96" + ], + "document_id": [ + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "b1ef905a-c145-5270-9110-ae6954ea3d72", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3" + ], + "id": [ + "chatcmpl-AIFi4Qsa1GjY5azJi3IYJdr8DLXln", + "2f35de05-41ee-5471-870d-a4e663cf32f6", + "1efa76cb-2289-5dd3-9fa5-776083aa5cd5", + "9faa9b6b-6a97-5979-bf49-8bbdb4bb383d", + "6d4a1a0b-2af3-5cc4-b7c0-a7223ce3edfa", + "45f74737-847a-52c2-a0b9-bf9de335a7ce", + "bd5fffd3-cf7a-5f67-b581-6cb803a48de4", + "27d74137-3987-571d-87ab-2c12ec66d1f7", + "180adffa-397c-599b-adb3-64a7f464aaaa", + "93b3cc74-a414-5097-802a-7dc2ad10171d", + "3593241d-677d-5042-a1e9-dd92760a8c0e" + ], + "contexts": [ + "senescence, exhausting the ability for a tissue to regenerate after injury, impacting mitochondrial function,and inducing protein aggregation. Senescent cells have altered metabolism, and they can secreteproinammatory factors and alter the local tissue environment, thereby contributing to aging andage-related degenerative diseases. In addition, stem cell function can be impacted by DNA damage by bothcell autonomous and nonautonomous mechanisms. Proper function of mitochondria is dependent upongenome", + "[87] and the accumulation of senescent cells in human tissues with age has been implicated as a driver of aging- related diseases. Indeed, pharmacological approaches targeting senescent cells, like senolytics, are a major and timely area of research that could result in human clin- ical applications [ 5,88]. It is imperative that we fully understand and deconstruct cellular senescence in order to target aging-related diseases. We hope that CellAge will help researchers understand the role that CS plays", + "An important source of inflammatory signals in aged organ- isms is thought to be the accumulation of senescent cells across tissues [ 5,7]. Indeed, accumulating evidence has shown that senescent cells are characterized by a senescence-associatedsecretory phenotype [ 810], which includes a panoply of pro-inflammatory cytokines, proteases, growth factors and metabolites [ 10,11]. The impact of senescent cells on age-related inflammation, and their potential role as a target for pro-", + "senescent cells [150]. SASP factors exert their functions in either an autocrine or a paracrine manner and are responsible for the induction of the chronic inflammation and cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu- mulation of senescent cells in tissue is closely associated with aging-related dis- eases. Recently, it was determined that senescent fibroblasts significantly increase the expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "atherosclerosis, osteoarthritis, sarcopenia, ulcer formation, cancer, and Alzheimer disease, which is suggestive of a causative role. However, the most convincing evidence that senescent cells causeaging comes from recent genetic (85) and pharmacologic studies (86) revealing that clearance of senescent cells can prevent or delay tissue dysfunction and extend health span. Senescent cells induce autocrine, as well as paracrine, signaling by secretion of proinamma-", + "senescence can deplete both stem (5153) and stromal (10,11) cell pools. Moreover, because senescent cellspersist, they have the ability to alter the tissue micro-environment, and can therefore also promote the degen-eration of organs and stem cell niches (14,46). Finally, senescent cells secrete factors such as matrix metallopro- teinase-3 (MMP-3), which favors extra-cellular matrixremodeling, promotes defects in epithelial cell dierentia-tion and stimulates cancer cell growth (46,54,55).", + "potential role of senescence in in vivo aging and disease has been difficult to assess and somewhat controversial [146]. However, recent studies have shown that senescent cells accumulate in normal arterial tissue over the lifespan of humans [147, 148]. Likewise, the accumulation of senescent cells has been reported in diseased tissues, such as atherosclerotic plaques [149] and abdominal aortic aneurysms [150]. Baker et al. showed that", + "51. Jeyapalan JC, Ferreira M, Sedivy JM, Herbig U. 2007. Accumulation of senescent cells in mitotic tissue of aging primates. Mech. Ageing Dev. 128:3644 52. Boyle J, Kill IR, Parris CN. 2005. Heterogeneity of dimer excision in young and senescent human dermal broblasts. Aging Cell 4:24755 53. Seluanov A, Mittelman D, Pereira-Smith OM, Wilson JH, Gorbunova V. 2004. DNA end joining becomes less efcient and more error-prone during cellular senescence. PNAS 101:762429", + "in many accelerated-aging mouse models and in a plethora of human age-associated pathologies, including osteoporosis, atherosclerosis, glomerular disease, diabetic venous ulcers, chronic ob-structive pulmonary disease and emphysema, osteoarthritis, herniated intervertebral discs, and vascular calcication (112). Senescent cells are resistant to apoptosis and accumulate exponen- tially with age as a consequence of inefcient clearance. Unlike apoptotic tissues, senescent tissues 436 VermeijHoeijmakersPothof", + "wound healing [ 8], and immune clearance [ 9,10]. By contrast, the gradual accumulation and chronic persistence of senescent cells with time promotes dele- terious effects that are considered to accelerate deterior- ation and hyperplasia in aging [ 11]. Senescent cells secrete a cocktail of inflammatory and stromal regula- torsdenoted as the senescence-associated secretory phenotype, or SASP which adversely impact neighbor- ing cells, the surrounding extracellular matrix, and other" + ], + "answer": "Senescent cells are cells that have stopped dividing and have entered a state of permanent growth arrest. They are characterized by an altered metabolism and the secretion of pro-inflammatory factors, a phenomenon known as the senescence-associated secretory phenotype (SASP) [1], [3]. These cells accumulate in tissues over time and are resistant to apoptosis, meaning they are not easily cleared from the body [9]. Senescent cells contribute to aging and age-related diseases in several ways. They impact the tissue environment by secreting inflammatory cytokines, proteases, and growth factors, which can lead to chronic inflammation and tissue dysfunction [3], [4]. This chronic inflammation is a significant factor in the development of age-related degenerative diseases [1], [4]. Additionally, senescent cells can alter the tissue microenvironment, promoting the degeneration of organs and stem cell niches, and potentially stimulating cancer cell growth [6]. The accumulation of senescent cells is associated with various age-related pathologies, such as atherosclerosis, osteoarthritis, and Alzheimer's disease [5], [9]. Recent studies have shown that clearing senescent cells can prevent or delay tissue dysfunction and extend health span, highlighting their causative role in aging [5].", + "question": "What are senescent cells and how do they contribute to aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_16 new file mode 100644 index 0000000..12b6b0f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2016 - The dog aging project translational geroscience in companion.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf" + ], + "extraction_id": [ + "6364d669-4b96-5d2f-8ce8-526b065dce72", + "30ba3324-6e19-58c2-9e32-508f827af3e5", + "b9f038dd-97af-51ea-bb32-d73bf66c3dcb", + "e433208e-665d-550c-b8e8-c9fb400f1159", + "915ca931-d49d-5837-97fd-f06c145764d0", + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "9770f6f4-b86a-514f-9cce-c23d2963aeae", + "21efa872-9d89-5dee-9dd1-27dcaa1208cf", + "bca61863-81b3-5ef7-850d-10cc9577a9e1", + "13ca8905-ddbb-5437-b6a8-4012969daa43" + ], + "document_id": [ + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "70945353-4808-539a-80f9-5632c27913e5", + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" + ], + "id": [ + "chatcmpl-AIFjcxBUYW02ZQfh6Ogs975bGNDi2", + "5fc33fac-ab39-5ec1-9fb9-dcaa93a595d3", + "4bf7307d-d8a0-5594-b0b5-487fe0f265ca", + "afc304d1-dd43-55ec-811d-27ca27fc4e5d", + "3fc1603d-dd9e-5bcf-96e6-6b927d344be1", + "7ca45b81-3f97-5b1b-9a84-84cfffc4cc08", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "193d98c7-8d37-5f83-b1b2-84aee242f079", + "7460a40c-8723-5de9-9f2e-c781f4872f1f", + "38c89363-89a1-56d5-82f2-28c19fa0fbcb", + "b9240ab4-370f-5bc1-8c33-9755ab788aac" + ], + "contexts": [ + "Dietary interventions, including starvation and protein deprivation, can also alter patterns of DNA methyla- tion, potentially in a long-lasting manner [42, 43], including transgenerationally [26, 44]. Dietary, genetic and pharmacological interventions that improve health during aging and extend lifespan induce long-lasting changes in gene expression that mediate their effects. Here we have asked if and how age-related DNA methylation, transcription and lipid", + "Longev. Heal. 2, 10 (2013). 7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. Aging Cell18, e12852 (2019). [PubMed: 30548460] 8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications for studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] 9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction, appear to extend not only life span, but also health span (Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does appear that it is possible to experimentally slow the rate of aging. Still, in each case, aging does continue on as if there is some", + "As we describe above, a small but growing number ofinterventions has been shown to reproducibly increase lifespan in laboratory animals and, in a few cases, to also delay or reverse age-related declines in multiple organsystems. These healthy aging interventions could, in prin- ciple, be tested to determine whether they also increase lifespan and promote healthspan in dogs (Table 1). There are several questions that immediately present themselves when considering the design of a healthy aging interven-", + "be linked to the biology of stem cell quiescence and self-renewal. Although genetic and environmental interventions have clearly proven to be effective in prolonging life span, we postulate thatthose interventions, as well as the rejuvenating interventions described above, are, in fact, acting primarily to modify theepigenome. Consistent with this, genetic interventions directlytargeting the epigenome can extend life span ( Greer et al., 2010 ). Studying aging and rejuvenation through the lens of", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "205. Li, Y.; Tollefsbol, T.O. p16INK4a Suppression by Glucose Restriction Contributes to Human Cellular Lifespan Extension through SIRT1-Mediated Epigenetic and Genetic Mechanisms. PLoS ONE 2011 ,6, e17421. [CrossRef] 206. Daniel, M.; Tollefsbol, T.O. Epigenetic linkage of aging, cancer and nutrition. J. Exp. Biol. 2015 ,218, 5970. [CrossRef] 207. Kapahi, P .; Kaeberlein, M.; Hansen, M. Dietary restriction and lifespan: Lessons from invertebrate models. Ageing Res. Rev. 2017 , 39, 314. [CrossRef]", + "as diabetes, cancer and neurodegenerative disorders [1, 2]. Environmental and genetic interventions can ameliorate the effects of aging, with nutrition, nutrient-sensing signaling networks and metabolism playing evolutionarily conserved roles [1, 3 5]. Diet- ary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di- verse model and non-model organisms [3, 6]. DR induces a remarkably broad-spectrum improvement in", + "53. Mair W & Dillin A Aging and survival: the genetics of life span extension by dietary restriction. Annu. Rev. Biochem. 77, 727754 (2008). [PubMed: 18373439] 54. Masoro EJCaloric restriction-induced life extension of rats and mice: a critique of proposed mechanisms. Biochim. Biophys. Acta1790, 10401048 (2009). [PubMed: 19250959] 55. Weindruch R, Walford RL, Fligiel S & Guthrie D The retardation of aging in mice by dietary", + "In addition to genes associated with aging, research has focused on identifying genes associated with the life- extending effects of CR. One method is to identify genesthat decrease or cancel out the life-extending effects of CRwhen mutated (Gems et al., 2002; Bishop and Guarente,2007). More than 100 such genes have been identified inmodel organisms (D. Wuttke, C. Vora, J. P. de Magalhes,unpublished observations). The growth hormone receptor(GHR) is the only gene so far identified in mammals that" + ], + "answer": "Yes, there are known lifestyle interventions that can positively impact genes related to aging. Dietary interventions, such as dietary restriction (DR) and calorie restriction, have been shown to alter patterns of DNA methylation and induce long-lasting changes in gene expression that improve health during aging and extend lifespan [1], [8]. These interventions can modify the epigenome, which is linked to the biology of aging [5]. Additionally, glucose restriction has been shown to extend human cellular lifespan through SIRT1-mediated epigenetic and genetic mechanisms [7].", + "question": "Are there any known lifestyle interventions that can positively impact genes related to aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_17 new file mode 100644 index 0000000..744f7be --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2022 - Functional genomics analysis identifies.pdf", + "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "2021 -Mozhui- Epigenetic aging.pdf", + "2021 - Genetic loci and metabolic states associated with murine epigenetic aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2019 - Improved precision of epigenetic clock.pdf", + "2021 - Genome-wide association studies identify.pdf" + ], + "extraction_id": [ + "a81cc7a6-0cc6-5909-9192-ac0fab26fbc2", + "63c7bfe5-a409-5435-91ea-487534957b81", + "6d7c1694-2c53-554c-9070-2db848fc5a42", + "c6cc3d8b-3736-5fe8-a4ff-eb186679a37e", + "c6cc3d8b-3736-5fe8-a4ff-eb186679a37e", + "6dfd0c51-91dd-5bb3-b7ae-a9c86ea22c35", + "68ee1ea3-5caf-5df5-8efc-134943a456cb", + "8f22afaf-a5fb-5f44-9fc2-18d4aeceede7", + "487cf1b1-1190-5d14-8b24-ba92f75aa6aa", + "53db6715-4f12-50ad-8fb9-acba4e2f4f37" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "1fe1c748-9e73-51ba-8521-de924cc133d4", + "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "d23daa43-4176-54e6-b3c3-b889843e92f1", + "b82bd9e1-2373-577b-a942-164565eaca6b", + "62b635c3-040e-512a-b016-6ef295308a1e", + "556d0179-023f-581f-9c2d-febe4e75722f", + "60c2e869-1fee-53ea-b332-26d9c2abc747" + ], + "id": [ + "chatcmpl-AIFji2gbFHCW8aj8mLegsooXneEeb", + "e2522b52-d927-5c1a-8569-8fcb706ecc1e", + "c76f4517-c117-56e6-96b9-218f0fdae9f3", + "4edf498a-20de-593a-b301-73c799b07691", + "99532996-c835-534a-b6e7-a2f95ec00e2c", + "cb09d819-b809-5844-a111-5c7c7b9f9a99", + "2d08a161-7a62-5d3f-b300-1ca93ee5b751", + "66c03d04-0af5-50e5-8d4a-9a645493db46", + "35c83256-6072-5e6a-b15e-0cae1991b034", + "39dfbf42-78ec-5b0a-8448-55f47c22830e", + "d5ae06ad-3d88-5c4f-972a-0510d2fc67f3" + ], + "contexts": [ + "vided one of the most reliable aging biomarkers. An epigenetic clock is a group of CpG sites with particular methylation patterns that are highly related to the chrono- logical age of an individual. This correlation is very robust (r=0.9) for individuals between 20 and 100years. The epigenetic clock is a breakthrough discovery that will allow novel experimental approaches to understand the biological basis of aging [113]. For example, by using the epigenetic clock as a measure of cellular", + "Epigenetic Clock Chronological age is the number of years a person has lived, and biological or phys- iological age refers to a measure of how well your body functions compared to your chronological age. Biological age is influenced by multiple factors (genes, lifestyle, behavior, environment, among others) and correlates with mortality and health sta- tus. The epigenetic clock is one potentially reliable predictor of biological age.", + "Background Epigenetic clocks are sets of CpG dinucleotides whose DNA methylation (DNAm) can be used to accurately predict a person s chronological age [ 1]. In recent years, various epigenetic clocks have been developed [ 25]. Well-known examples are the clocks de- veloped by Hannum et al., trained on blood samples and containing 71 CpGs [ 2], and Horvath, a multi-tissue predictor consisting of 353 CpGs [ 3]. A popular application of", + "An EpigeneticClock The aging transcriptome could be used to gauge the physiological age of worms, and in that way serve as an epigenetic clock revealing how much of life span has been spent and how much remains (23). Middle-aged worms show an aging transcriptome half-way between the aging expression profiles of young and old worms. This provides an independent way to assess the age of an animal independent of its life span. This is important as there are at least 2 explanations to", + "The epigenetic aging clock measures the sum of all the age-related pathways affecting cellular physiology in old age. The aging epigen- etic clock is heavily enriched for germline- and intestinal-expressed genes, but lack muscle- and neuronal-expressed genes (23, 25). Expression changes in the germline and intestine were expected as there are massive changes in the morphology of gonad at the end of fertility and the intestine in old age. The aging transcriptome pro-", + "etic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism. Introduction Epigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE *For correspondence: kmozhui@uthsc.edu Competing interest: See page 22 Funding: See page 22", + "etic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism. Introduction Epigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These DNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE *For correspondence: kmozhui@uthsc.edu Competing interest: See page 22 Funding: See page 22", + "estimators epigenetic clocks; telomere length; transcriptomic-, proteomic-, and metabolomic-based estimators; and composite biomarkers concluded that the epi- genetic clock is the most promising molecular estimator of biological age [26]. Epigenetic age estimators are sets of CpGs (also known as clock CpGs) that are coupled with a mathematical algorithm to estimate the age of a DNA source, such as cells, tissues, or organs. This estimated age, also referred to as epigenetic age or", + "proved epigenetic clock. It should be noted that building a biological age predictor is difficult since there is no clear definition of biological age. Nevertheless, one of the essential features of biological age is its ability to in- dicate the different ageing rates between individuals with the same chronological age. A previous study has re- ported a number of CpG sites that show variation in the longitudinal changing rates between individuals [ 40].", + "ranging from 0.15 to 0.19 [ 8,9]. Individuals with epigenetic clock estimates greater than their chronological age display age acceleration and have been shown to be at a greater risk of all-cause mortality and multiple adverse health outcomes [ 10]. Conse- quently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field [ 11]. The first generation of epigenetic aging clocks used penalized regression models to" + ], + "answer": "The 'epigenetic clock' is a molecular biomarker of aging that is based on the DNA methylation levels of specific CpG sites. These methylation patterns are highly correlated with an individual's chronological age, with a robust correlation coefficient of approximately 0.9 for individuals aged between 20 and 100 years [1]. The epigenetic clock serves as a reliable predictor of biological age, which refers to how well a person's body functions compared to their chronological age [2]. In aging research, the epigenetic clock is used to estimate the biological age of cells, tissues, or organs by analyzing the methylation levels of select CpGs, often referred to as clock CpGs [8]. This estimated age, known as the epigenetic age, can indicate different aging rates between individuals with the same chronological age, providing insights into the biological basis of aging [9]. The epigenetic clock has been applied in various studies to understand the relationship between epigenetic aging and factors such as metabolism, and it is considered one of the most promising molecular estimators of biological age [6], [8].", + "question": "What is the 'epigenetic clock,' and how is it used in aging research?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_18 new file mode 100644 index 0000000..4fa7cac --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Longevity Genomics Across Species.pdf", + "2016 - Epigenetics and aging.pdf", + "2022 - Genome-wide screens in yeast models towards.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2012 - Replicative and Chronological Aging.pdf", + "2012 - Genomics and Successful Aging Grounds for Renewed.pdf", + "2016 - Unraveling the message insights into comparative genomics.pdf", + "2016 - Epigenetic Mechanisms of Longevity and Aging.pdf", + "1989 - Genetic instability and aging theories, facts, and future perspectives.pdf", + "2016 - Epigenetics and aging.pdf" + ], + "extraction_id": [ + "789baa84-292c-547a-8600-8568f34ea9de", + "6b4e8da8-7d82-5531-b1a8-21935a6f4a51", + "6977180a-bec7-5d39-ba97-b0fd664b6c96", + "fbb608d4-8b2b-502d-88cc-d1fcb0b89afb", + "cb936ad1-1a4f-5b1c-89f1-f8991f059969", + "3d18e792-3d83-5cc3-b9ab-309322ecf55d", + "0dc4e4f4-cd48-5ccd-991b-edd688533b5e", + "5ff88a27-4525-5e7f-99bd-e781cedd8005", + "b0531531-f629-512b-9835-24cc870b4ef3", + "6b4e8da8-7d82-5531-b1a8-21935a6f4a51" + ], + "document_id": [ + "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "be6ba237-7f13-5aac-8cb1-6a43e82d326b", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "496e387e-4278-5f74-8ecc-4edc1cee7dfe", + "6d2b82c3-4256-562a-9b23-ff7c71e9fd93", + "0deba7bb-c27a-5d9e-b1b2-e48a5574882c", + "588185a0-e157-552f-a304-4beefb85d398", + "4d5b1800-b676-5865-a555-09ea740cc14a", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d" + ], + "id": [ + "chatcmpl-AIFlJI3UdMQFpFb34IWrf77DCGGJY", + "f125dd77-ecd5-59aa-9cf0-ae89333159d2", + "35414229-a946-525c-b508-4b8f49a2702c", + "1b3d84fb-c799-5d19-b3bd-a9032b7980fc", + "5caecfbd-14ef-59e2-a281-2bc524ca0353", + "c14402ec-2ad7-5857-9f09-39c71656bf0f", + "c103f3f8-b155-5787-bdd9-16f9d390369d", + "b19ebe3b-e87e-5cab-baef-24deddd303bb", + "c32f3dbe-95d5-531a-9165-d4da7b2dc2a8", + "91375d45-be1d-5c54-8d0f-a9b1dded69bb", + "ae5be149-52ad-5854-b40a-c24374545cf0" + ], + "contexts": [ + "the nematode Caenorhabditis elegans , and the budding yeast Saccharomyces cerevisiae , have emerged as the most widely used and, hence, best characterized, model organisms in bio- gerontology. When considering the use of simple eukaryotes to study aging and age-related disease, it is pertinent to ask whether, and to what degree, the aging process is evolutionarily con- served. Does a yeast cell age by the same mechanism(s) as a", + "Studies on the aging of mammals are rather limited by the long life span of the commonly used model organisms. Thus, both nonverte-brate and invertebrate organisms, with their shorter life span and ease of genetic and environmental manipulations, gained popularity amongresearchers in the aging field as experimental models for aging studies. Among them, budding yeast or Saccharomyces cerevisiae is a highly in- formative organismal model for aging studies with its genetic tools,", + "Abstract Cellular models such as yeasts are a driving force in biogerontology studies. Their simpler genome, short lifespans and vast genetic and genomics resources make them ideal to characterise pro-ageing and anti-ageing genes and signalling pathways.Over the last three decades, yeasts have contributed to the understanding of fundamental aspects of lifespan regulation including the roles of nutrient response, global protein translation rates and quality, DNA damage, oxidative stress,", + "usually chosen for convenience rather than for specific features applicable to human aging. Hence, choosing the suitable animal model to answer the specific question we aim to understand is of high importance in these types of studies. Among the most prevalent aging model organisms are Saccharomyces cerevisiae , Caenorhabditis elegans, Drosophila melanogaster, and Mus mus - culus . As a single-celled organism, S. cerevisiae is easily grown,", + "mammalian genes that affect aging than any other model organism. Aging in yeast is assayed primarily by measurement of replicative or chronological life span. Here, we review the genes and mechanisms implicated in these two aging model systems and key remaining issues that need to be addressed for their optimization.", + "be more exaggerated in more distantly related species (such as the worm and mouse models). There are, however, simi - larities between aged humans and aged model organisms; they all tend to have decreasing overall fitness, and there - fore, studies using model organisms continue as they may be at least indicative of some aging mechanisms in humans. Extensions to life span in model organisms are mostly associated with disruption to fundamental metabolic path -", + "eukaryote model organisms, namely yeast, worms, ies,and sh, as well as mice and rats, to explore both genetic and environmental determinants of lifespan. While these short-lived models have each yielded a number of fasci- nating ndings and insights into hypotheses surrounding extended lifespan and healthspan, they may also haveconstrained this complex, multifactorial eld to areas in which they are best suited, most notably short-term inter-", + "et al., 2010 ). These effects require an intact germline, andTable 2. Repositories and Tools for Aging Research Models Description Link/Reference Yeast Saccharomyces genome database http://www.yeastgenome.org/ published lifespan data http://lifespandb.sageweb.org/ (McCormick et al., 2015 ) Wilcoxon rank sum test to test signicance of lifespan differenceshttp://data.kaeberleinlab.org/scripts/ranksum.php yeast outgrowth data analyzer (YODA) for chronological lifespan assayshttp://yoda.sageweb.org/", + "for molecular biological studies on aging. Although material from humans should be employed where possible, for prac- tical reasons animal model systems like rats and mice are indispensible. There is evidence that, provided their health sta- tus and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978). For studying certain funda- mental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also", + "short life span, and fully sequenced genome (20 ,21). Despite being uni- cellular, yeast has been an excellent model to identify and characterize conserved basic biological processes, including aging. Yeast has beenextensively used to identify genes and interventions responsible for lifespan extension and to gain insights into the aging processes of all eu- karyotic organisms. In parallel, over the years, studies on invertebrate organisms, such as Drosophila melanogaster (flies) and Caenorhabditis" + ], + "answer": "Researchers use model organisms like yeast and worms to study human aging due to their simpler genomes, short lifespans, and the ease with which they can be genetically and environmentally manipulated. These characteristics make them ideal for identifying and characterizing genes and signaling pathways involved in aging [3]. Yeast, specifically Saccharomyces cerevisiae, is a highly informative model for aging studies because of its genetic tools and the ability to measure aging through replicative or chronological lifespan assays [2], [5]. Yeast has been extensively used to identify genes and interventions responsible for lifespan extension, providing insights into the aging processes of all eukaryotic organisms [10]. Similarly, the nematode Caenorhabditis elegans is another widely used model organism in biogerontology. Researchers study these organisms to understand whether the aging process is evolutionarily conserved and to what degree mechanisms in these simpler organisms can be indicative of aging mechanisms in humans [1], [6]. These model organisms help explore both genetic and environmental determinants of lifespan, contributing to hypotheses surrounding extended lifespan and healthspan [7].", + "question": "How do researchers use model organisms like yeast or worms to study human aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_19 new file mode 100644 index 0000000..f94bd0d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2001 - Demography in the age of genomics.pdf", + "2020 - Protecting the Aging Genome.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Spontaneous DNA damage to the nuclear genome promotes senescence.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2009 - Adaptation, aging, and genomic information.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2009 - MicroRNAs in C. elegans Aging Molecular Insurance for Robustness.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf" + ], + "extraction_id": [ + "68b12e10-d643-570a-aa63-eda62de81928", + "e3014138-3d5b-58bc-a1a5-5ac6f04cac1c", + "e5067ce2-69a6-5433-bed4-b95daeaa691e", + "822571e2-b05d-5e17-9eaa-431151851111", + "005e73b5-7a93-53ff-946c-735fb4588de5", + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "c2a8f947-44f2-5100-99e5-9c3a2f1284e9", + "8650652a-1765-563b-a98e-2e9336bcf29a", + "c8d6f90d-a25c-590a-a546-4500df09aa28", + "6c9e1997-bfe6-5708-a476-07c833eed8fa" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "bb774030-2570-5596-b2ab-b8f57ff81086", + "62b635c3-040e-512a-b016-6ef295308a1e", + "08be7274-78a3-5e93-9e8c-3d4f6dbeacf9", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "54a993af-b86b-5cc3-a04b-bab03c244534", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "dff49223-ac74-5419-a190-a0c7f43a5ee5", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b" + ], + "id": [ + "chatcmpl-AIFlT2nob40QrExWjGMMqZ4fSc8yC", + "78733c6a-d870-5154-9128-eb66291fa967", + "9da7c5dc-0deb-577c-bb22-83f987bd76dd", + "3c636897-c47e-505d-9203-306124b73e0e", + "265126e3-2a4d-518f-93cf-21a201747eef", + "dcc13291-f18b-5094-83b6-4609322bc242", + "1c4286b6-ede2-568b-9c18-b1e99ede17a6", + "2c5241f1-1655-5e36-a787-b966767b2534", + "f20fd517-5f05-53ca-93a5-916bc891ad92", + "69681eeb-6629-5091-b2b4-b4444e570913", + "5d8cc04f-7e13-5dbc-80c2-a35643954e9a" + ], + "contexts": [ + "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections. Prospective Epidemiologic Surveys that Include Genetic Information Some epidemiologic cohort studies of populations have collected", + "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi- ple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage. Future Prospects: Developing Interventions through DNA Repair", + "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705. 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov- ery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190. 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "J.L. Kirkland, Barriers to the Preclinical Development of Therapeutics that Target Aging Mechanisms, J. Gerontol. A Biol. Sci. Med Sci. 71 (11) (2016) 1388 1394 . [2]D.J. Baker, B.G. Childs, M. Durik, M.E. Wijers, C.J. Sieben, J. Zhong, R.A. Saltness, K.B. Jeganathan, G.C. Verzosa, A. Pezeshki, K. Khazaie, J.D. Miller, J.M. van Deursen, Naturally occurringp16(Ink4a)-positive cells shorten healthy lifespan, Nature 530 (7589) (2016) 184 189.", + "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "pharmaceutical and other interventions for human aging based on research that starts with the genomic information required to sustain adaptation, and thus health, in older fruit flies [36-39]. Naturally, any such genomic short-cut to reverse-engineering the evolution of slowed aging from fruit flies to humans is fraught with potential for error. Such evolutionarily deep orthologies are sure to supply", + "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "[7] Hughes, S.E., Evason, K., Xiong, C., Kornfeld, K. Genetic and pharmacological factors that influence reproductive aging in nema- todes. PLoS Genet. 2007 , 3: e25. [8] Vijg, J., Campisi, J. Puzzles, promises and a cure for ageing. Na- ture 2008 , 454: 1065-1071. [9] Rolland, Y., Czerwinski, S., Abellan Van Kan, G., Morley, J.E., Cesari, M., Onder, G., Woo, J., Baumgartner, R., Pillard, F., Boirie, Y., Chumlea, W.M., Vellas, B. Sarcopenia: its assessment, etiol-", + "for the aging process during the 20th Century. Thissituation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology. We propose such astrategy. As a rst step, we recommend the use of modelsystems in which signicant genetic intervention is not proscribed or impractical. Second, we propose that work" + ], + "answer": "Yes, there are promising anti-aging therapies being developed based on genetic research. Several approaches are being explored: 1. **Senolytic Drugs**: Research has shown that abolishing senescent cells through genetic manipulation or senolytic drugs can significantly halt biological aging in mice. Trials are underway to test the ability of senolytics to postpone age-associated pathologies in humans [3]. 2. **Genetic Discoveries in Aging**: A number of genes capable of altering the aging process have been identified in animal models and even in humans. This area of research is promising as it explores the association of multiple alleles with human exceptional longevity [6]. 3. **Manipulation of Aging-Related Genes**: There is potential in manipulating aging-related genes through diet, lifestyle, and pharmaceuticals to improve human health and develop drugs against age-related diseases such as cancer, heart disease, type 2 diabetes, obesity, and neurodegenerative diseases [8]. These developments indicate that genetic research is paving the way for potential anti-aging therapies.", + "question": "Are there any promising anti-aging therapies being developed based on genetic research?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_2 new file mode 100644 index 0000000..58edb8f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Human Ageing Genomic Resources Integrated.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "1999 - Molecular Biology of Aging.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2013 - Gene expression changes with age in skin.pdf", + "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", + "2013 - Gene expression changes with age in skin.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2007 - Characterization of a bidirectional promoter shared between two human.pdf" + ], + "extraction_id": [ + "25e9d8a3-54ac-5412-8efb-3b56d93f363f", + "68b12e10-d643-570a-aa63-eda62de81928", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "66c6e911-2b6e-5d79-9e13-9f0e370c6fa6", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "18e9f96b-9af8-59bf-9323-084df9a4f9b6", + "39cc0d86-f175-5fe3-990f-8471c7395ba4", + "dfb687b2-f1ff-5e22-8a67-4a1db9ebeb3c", + "68b12e10-d643-570a-aa63-eda62de81928", + "a5581fce-8af9-5131-a94b-8fce7fbf9557" + ], + "document_id": [ + "5f554cc7-c94d-5fbd-9567-528499663ed6", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "6f122d3a-d8ca-598f-8767-c059a941cef3", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e9a81a0d-52b4-5ced-be05-130eed317085" + ], + "id": [ + "chatcmpl-AIFgbQ1pPISWJITI2YHN6poeJtTyV", + "0ae63c75-df5f-59b0-9561-30d5115f0f74", + "c258bd44-b5b3-5eb5-9c72-60b365f18f81", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "0671ed95-2909-54ac-baea-b156ee0ea8f8", + "650300e1-898c-56e2-9358-0bb6625b0073", + "cc78a209-081f-5e0c-877a-36b85d5105a6", + "a3859151-a94e-5085-8e7a-3c1d50716319", + "d53018ae-0881-5ef4-9c49-48623e8aa342", + "a4773f1a-f2d3-5950-a81e-d22357e97a0f", + "91530253-7015-5b7a-bb7b-506ddeb3c3f8" + ], + "contexts": [ + "potentially associated with human ageing. For eachgene, a description compiled from the studies that linkthe gene to ageing is provided. It should be noted thatour focus is on genes that might affect the ageingprocess, rather than individual age-related pathologies; genes affecting multiple, even if not all, age-related", + "showing that single genes can regulate aging in modelorganisms demonstrate that aging can be geneticallymanipulated (Finch and Ruvkun, 2001; Kenyon, 2010).Hundreds of genes that modulate longevity have nowbeen identified in model organisms (de Magalha es et al.,2009a). In some cases (e.g., in worms), mutations insingle genes can extend lifespan by almost 10-fold (Ayy-adevara et al., 2008). Nonetheless, aging is a complexprocess that derives not from single genes but from theinteractions of multiple genes", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "key genes and pathways important in aging; geneticstudies of heritable diseases that cause the appearanceof premature aging in affected people; physiological ex-Introductionperiments that relate the pace of aging to caloric intake;Is aging the final act in the script of developmental biol-and advances in human genetics, as well as cell andogy? The characteristic changes that are part and parcelmolecular biology leading to an understanding of theof aging appear similar to developmentally regulatedbasis of", + "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky et al .,2007, 2008) and exhibiting slower molecular evolution rates (de Magalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging. To allow researchers to focus specifically on human aging,", + "expression of certain genes have an effect upon longevity. Although similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "complex.108,109Studies on models such as the yeast Sac- charomyces cerevisiae110the nematode Caenorhabditis elegans,111the fly Drosophila melanogaster,112-114the mouse Mus musculus,115and humans116show that single gene mutations can contribute to the initiation of aging andinduce premature aging syndromes. There are, however, nospecial genes that can cause aging-associated damages. Themanifestation of aging is mostly due to the failure of main-tenance and repair mechanisms. 117,118", + "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes [4]. A study of postmortem human brain tissue from 30 individuals aged 26 to 106 years [5] showed that approxi- mately 4% of approximately 11,000 genes analyzed show a significant age-related expression change (1.5-fold or more) in individuals aged >40 years. These genes were reported to play central roles in synaptic plasticity, vesi- cular transport, and mitoch ondrial function. Another", + "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "[2] L. Partridge, D. Gems, Mechanisms of ageing: public or private? Nat. Rev. Genet. 3 (2002) 165 175. [3] A.M. Leroi, et al., What evidence is there for the existence of individual genes with antagonistic pleiotropic effects? Mech. Ageing Dev. 126 (2005)421429. [4] S.N. Austad, Is aging programmed? Aging Cells 3 (2004) 249 251. [5] V.D. Longo, J. Mitteldorf, V.P. Skulachev, Opinion: programmed and altruistic ageing, Nat. Rev. Genet. 6 (2005) 866 872." + ], + "answer": "Genes affect the aging process in humans through complex interactions and pathways. Research has shown that single genes can regulate aging in model organisms, indicating that aging can be genetically manipulated [2]. Hundreds of genes have been identified that modulate longevity in these organisms, and some of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, have been shown to affect longevity across different species, suggesting that some mechanisms of aging are evolutionarily conserved [3]. In humans, it has been more challenging to identify specific longevity candidate genes, but studies have shown that certain genes associated with aging in model organisms are evolutionarily conserved and may be relevant to human aging [5]. Additionally, approximately 4% of genes analyzed in a study of postmortem human brain tissue showed significant age-related expression changes, indicating that these genes play central roles in processes like synaptic plasticity, vesicular transport, and mitochondrial function [8]. Overall, while aging is a complex process involving multiple genes and their interactions with the environment, genetic studies in model organisms provide insights that may be applicable to understanding human aging [9].", + "question": "How do genes affect the aging process in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_20 new file mode 100644 index 0000000..3f2af79 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "2009 - Genes and gene expression modules associated with caloric.pdf", + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2007 - The role of nuclear architecture.pdf", + "2016 - Epigenetics and aging.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "2008 - Longevity mutation in SCH9 prevents recombination.pdf", + "2008 - DNA damage and ageing.pdf" + ], + "extraction_id": [ + "db18d213-92ed-5f3e-a242-60d3ed0ec8c8", + "0b45ae60-562c-5e48-a1c1-9eb29614a63c", + "a29560f5-a9e0-56d0-95f2-138eef516ef5", + "9655c555-838e-5cdf-85cf-13736c3cf028", + "91434549-bda3-5154-b089-28efed9c1089", + "5745c701-a549-51c3-adcc-b19c47436740", + "f4edcf22-ca64-5fe6-9bfa-f97a017d2d08", + "381d5314-5aa1-525f-a2a5-352b70ad86fa", + "bc856edb-7526-5424-a822-47075459a607", + "554b2b00-d006-5b97-aeb1-70ec31482641" + ], + "document_id": [ + "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "893ba204-2e69-563f-9046-7246ca61494f", + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "a6b022ba-653f-51d3-845a-dd6b3d61d4a4", + "f170e9cf-dfab-5758-ab23-08daff2af694" + ], + "id": [ + "chatcmpl-AIFlaWmRr9Bl0RbY7T3uxkIANSmkA", + "56e72d29-31c2-5096-b4f8-06c740bce06e", + "2b081115-d36e-57ec-aedc-2fd9691bc5e9", + "bb028469-8295-5657-8061-a715cb314a4a", + "e01c4c58-342d-5369-89e6-98344af55000", + "1e116f55-36fd-525f-9950-9a1354c32f7b", + "b990eb0a-709a-500c-836e-83e202e0d6a6", + "833c9ddc-ec27-5301-9c3b-025eab95e28c", + "8151fbcb-f498-56a7-84ce-8af647fd2245", + "537cff80-380d-5c6a-a25a-04b32e9b1bd7", + "4f28b643-04a4-59e2-8226-ab050e698b64" + ], + "contexts": [ + "caloric restriction. Physiol. Genom. 17, 307 315.Van Remmen, H., Ward, W.F., Sabia, R.V ., Richardson, A., 1995. Gene expression and protein degradation. In: Masoro, E.J. (Ed.), Handbook ofPhysiology. Section 11: Aging. Oxford University Press, New York, pp. 171234. Weindruch, R., Walford, R.L., 1982. Dietary restriction in mice beginning at 1 year of age: effect on life-span and spontaneous cancer incidence.Science 215, 1415 1418.S.R. Spindler / Mechanisms of Ageing and Development 126 (2005) 960 966 966", + "extension by dietary restriction. Annu Rev Biochem 2008, 77:727-54. 8. Harper JM, Leathers CW, Austad SN: Does caloric restriction extend life iin wild mice? Aging Cell 2006, 5:441-9. 9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J 2003, 17:690-2. 10. Spindler SR, Mote PL: Screening candidate longevity therapeu- tics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "analysis in calorie-restricted rats implicates epigenetic and post-translational mechanisms in neuroprotection and aging. Genome Biol. 2015;16:285. 21. Gillespie ZE, Pickering J, Eskiw CH. Better living through chemistry: caloric restriction (CR) and CR mimetics alter genome function to promote increased health and lifespan. Front Genet. 2016;7:142. 22. Jiang T, Liebman SE, Lucia MS, Phillips CL, Levi M. Calorie restriction modulates renal expression of sterol regulatory element binding proteins, lipid", + "Calorie restriction, a dietary regimen that extends the lifespan of numerous organisms, also delays the majority of age-related gene-expression changes in mice and, to a certain extent, in flies45,50. It is currently unclear whether the effect of calorie restriction on gene expression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest that there may be a causal link: Sir2 not only facilitates heterochromatin and promotes DNA stability, but is", + "Transcriptome analysis in calorie-restricted rats implicates epigenetic and post- translational mechanisms in neuroprotection and aging. Genome Biol. 16,2 8 (2015). 204. M. V. Blagosklonny, Calorie restriction: Decelerating mTOR-driven aging from cells to or- ganisms (including humans). Cell Cycle 9, 683 688 (2010). 205. D. K. Ingram, G. S. Roth, Calorie restriction mimetics: Can you have your cake and eat it, too? Ageing Res. Rev. 20,4 662 (2015).", + "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science 289:21262128. Mair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary restriction and death in Drosophila. Science 301:17311733. Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126:913922. Mathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap- proaches. Mech Ageing Dev 127:584589. Meric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "Keywords: Caloric restriction; Short-term; Longevity; Cancer; Microarray; Affymetrix Aging is widely assumed to result from the gradual age- related accumulation of essentially irreversible moleculardamage. In this context, CR is often viewed as preventing orslowing the accumulation of such damage, thereby slowingthe process of aging ( Bokov et al., 2004 ). This view is intuitively appealing, as it provides a straightforwardexplanation for the stochastic nature of aging and the onset", + "of short- and long-term caloric restriction effects in the liver of agingmice. Proc. Natl. Acad. Sci. U.S.A. 98, 10630 10635.Capstick, F., Brooks, B.A., Burns, C.M., Zilkens, R.R., Steinbeck, K.S., Yue, D.K., 1997. Very low calorie diet (VLCD): a useful alternative inthe treatment of the obese NIDDM patient. Diab. Res. Clin. Pract. 36, 105111. Chen, H., 2004. Gene expression by the anterior pituitary gland: effects of age and caloric restriction. Mol. Cell. Endocrinol. 222, 21 31.", + "genomic effects of caloric restriction. Mech. Ageing Dev. 126 : 960 966 . Sun , H. , R.J. Bennett , and N. Maizels . 1999 . The Saccharomyces cerevisiae Sgs1 helicase effi ciently unwinds G-G paired DNAs. Nucleic Acids Res. 27 : 1978 1984 . Thompson , L.H. , and D. Schild . 2002 . Recombinational DNA repair and human disease. Mutat. Res. 509 : 49 78 .", + "L. & Spindler, S. R. Genomic profiling of short- and long-term caloric restriction effects in the liver of aging mice. Proc. Natl Acad. Sci. USA 98, 1063010635 (2001). 62. Harman, D. The aging process. Proc. Natl Acad. Sci. USA 78, 71247128 (1981). 63. van der Pluijm I, G. G.et.al. Impaired genome maintenance suppresses the growth hormoneinsulin-like growth factor 1 axis in mice with cockayne syndrome. PLoS Biol. 5, 2338 (2006). 64. Yang, H., Baur, J." + ], + "answer": "Caloric restriction and diet have significant impacts on the genetics of aging through various mechanisms: 1. **Gene Expression and Lifespan Extension**: Caloric restriction (CR) has been shown to delay age-related gene-expression changes in mice and, to some extent, in flies. This suggests that CR may influence the genetic pathways associated with aging, potentially contributing to lifespan extension [4]. 2. **Epigenetic and Post-Translational Mechanisms**: In calorie-restricted rats, transcriptome analysis indicates that CR involves epigenetic and post-translational mechanisms, which are implicated in neuroprotection and aging. These mechanisms may alter genome function to promote increased health and lifespan [3], [5]. 3. **mTOR Pathway**: Caloric restriction is associated with decelerating mTOR-driven aging, which is a significant pathway involved in cellular growth and metabolism. By modulating this pathway, CR may influence the genetic regulation of aging processes [5]. 4. **Genomic and Epigenetic Approaches**: Nutritional modulation, including caloric restriction, can impact aging through genomic and epigenetic approaches. This suggests that diet can influence the genetic and epigenetic landscape, potentially affecting the aging process [6]. Overall, caloric restriction and diet can modulate genetic pathways and mechanisms that are crucial for aging, potentially leading to increased lifespan and improved health during aging.", + "question": "How do caloric restriction and diet impact the genetics of aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_3 new file mode 100644 index 0000000..32565a3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2009 - DNA Damage, Aging, and Cancer.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2011 - A genomic analysis of chronological longevity.pdf", + "2004 - A Transcriptional Profile of Aging.pdf", + "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2003 - Lifelong voluntary exercise in the mouse prevents.pdf" + ], + "extraction_id": [ + "21efa872-9d89-5dee-9dd1-27dcaa1208cf", + "b03f4297-85f4-5011-8dcf-ec169d3051d3", + "30ba3324-6e19-58c2-9e32-508f827af3e5", + "791bae8d-8d24-5873-b611-9c289591d11d", + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "d7daf4ea-f57a-5f7b-b6f7-afae08c35b45", + "b382fe8a-0267-5515-ac4b-07be55420040", + "6364d669-4b96-5d2f-8ce8-526b065dce72", + "86f9502b-7a3a-501f-9053-8af1d37043b4", + "e6c82594-27ba-5754-a106-69ae8b5e72ae" + ], + "document_id": [ + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "630c29c7-1dd7-509e-9b6b-b4af98b4ea48", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "a2e69cf7-8475-55f6-8fab-a572c12de9f0", + "4ab656a7-9656-526b-94e1-422875409b44", + "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "24d4f270-f45b-5830-84f9-b1e5bcd3c070" + ], + "id": [ + "chatcmpl-AIFgiWkzt5opfBd5VTvAKGVKegG8y", + "7460a40c-8723-5de9-9f2e-c781f4872f1f", + "d78564d5-d785-554a-bb2c-d71917ccfe19", + "4bf7307d-d8a0-5594-b0b5-487fe0f265ca", + "da620f88-db92-5267-af81-d6b548e9f29c", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "a4e0cb76-8950-5471-a3c1-1ed43094fdf3", + "1da274d3-c789-5af5-a8b5-72cdc9a01899", + "5fc33fac-ab39-5ec1-9fb9-dcaa93a595d3", + "321d14fd-f2ae-5904-b502-dae3491cd370", + "4c3d343d-d443-5bb4-a9ef-dd1eecaf9fac" + ], + "contexts": [ + "as diabetes, cancer and neurodegenerative disorders [1, 2]. Environmental and genetic interventions can ameliorate the effects of aging, with nutrition, nutrient-sensing signaling networks and metabolism playing evolutionarily conserved roles [1, 3 5]. Diet- ary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di- verse model and non-model organisms [3, 6]. DR induces a remarkably broad-spectrum improvement in", + "limiting exposure to exogenous genotoxins and by suppressing metabolism thereby producing fewer reactive species. However, DNA damage, like caloric restriction, can also elicit a protective survival response that promotes longevity and healthy aging. Recently, the use of sirolimus in mice was found to extend their life span and de - lay the development of conditions associated with aging, including cancer. 1 Sirolimus is one of pre -", + "Longev. Heal. 2, 10 (2013). 7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. Aging Cell18, e12852 (2019). [PubMed: 30548460] 8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications for studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] 9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "can be slowed down to some extent by eating a healthy diet and taking physical exercise, and many of the chronic diseases prevalent in older adults are either preventable or modi able with healthy lifestyle habits. Thus, older adults can experience successful aging that allows them to achieve physical, social and mental well - being over the life course and to participate in society. Much research has been conducted in recent years to", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "13,14 Prior studies have identified dozens of genetic and environ - mental modifiers of chronological or replicative longevity, some of which are now known to function similarly to modulate life span in multicellular eukaryotes. 15-17 One example of such a con - served longevity intervention is dietary restriction, which has been shown to slow aging in many different species including yeast, nematodes, fruit flies and rodents, 18,19 and most recently", + "Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellularprocesses (such as mitochondrial function, chromatin regu- lation, insulin signaling, tran scriptional regulation, and genome stability). This indicates that aging is a complex process driven by diverse molecular pathways and biochem- ical events. As such, a powerful approach to study aging is touse systems biology, which allows a multitude of factors", + "Dietary interventions, including starvation and protein deprivation, can also alter patterns of DNA methyla- tion, potentially in a long-lasting manner [42, 43], including transgenerationally [26, 44]. Dietary, genetic and pharmacological interventions that improve health during aging and extend lifespan induce long-lasting changes in gene expression that mediate their effects. Here we have asked if and how age-related DNA methylation, transcription and lipid", + "in yeast , Drosophila, and C. elegans is able to slow aging and increase lifespan [252-255]. Follow -up stud ies out of Richard Millers laboratory reproduced these findings in mice fed a diet with rapamycin incorporated [256, 257]. These studies suggested that inhibiting mTOR via rapamycin could delay age-associated diseases and extend lifespan in mammals. A subsequent study replicated these findings by genetically manipulating a", + "appears to retard aging at the molecular level as indi-cated by the gene expression analysis? Most likely,aging retardation at the molecular level by exercise isnot observed in all tissues, including some that maylimit lifespan. For example, if exercise does not reduceaging rates in replicative tissues, then it will not retardage-related tumor onset, which tends to limit maxi-mum lifespan. Another possibility relates to the obser-vation that wheel running decreased to an average 680m/day at 33 mo of age" + ], + "answer": "To slow down genetic aging, several lifestyle choices can be beneficial: 1. **Dietary Restriction (DR)**: Reducing food intake without causing malnutrition has been shown to extend lifespan in various organisms. This approach improves healthspan by influencing nutrient-sensing signaling networks and metabolism [1]. 2. **Healthy Diet and Physical Exercise**: Engaging in regular physical activity and maintaining a healthy diet can slow down aging and prevent or modify many chronic diseases prevalent in older adults. This contributes to successful aging, allowing individuals to achieve physical, social, and mental well-being [4]. 3. **Caloric Restriction**: This is considered a reasonable anti-aging intervention, as it has been shown to slow aging in multiple species, including yeast, nematodes, fruit flies, and rodents [6]. 4. **Exercise**: While exercise may not retard aging in all tissues, it can contribute to molecular-level aging retardation, which is beneficial for overall health and longevity [10]. These lifestyle choices, when combined, can help mitigate the effects of genetic aging and promote a longer, healthier life.", + "question": "What lifestyle choices can help slow down genetic aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_4 new file mode 100644 index 0000000..b8cde0c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1989 - Genetic instability and aging theories, facts, and future perspectives.pdf", + "2006 - Specific age related signatures in Drosophila body parts.pdf", + "2016 - The dog aging project translational geroscience in companion.pdf", + "2009 - MicroRNAs in C. elegans Aging Molecular Insurance for Robustness.pdf", + "2012 - Genomics and Successful Aging Grounds for Renewed.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2011 - Genomics of human longevity.pdf", + "2001 - Demography in the age of genomics.pdf", + "2021 - Lifespan-Associated Gene Expression Signatures of Recombinant BXD Mice Implicates Coro7 and Set in Longevity.pdf" + ], + "extraction_id": [ + "b0531531-f629-512b-9835-24cc870b4ef3", + "efba6890-9b12-567c-b3f0-4e6ff5c6e9c4", + "9c8bc002-4f7d-5c53-9736-70f59a6ee518", + "c8d6f90d-a25c-590a-a546-4500df09aa28", + "3d18e792-3d83-5cc3-b9ab-309322ecf55d", + "bfeb5c38-4fa6-5df5-90ce-63204deba3a8", + "396683f9-b2e3-5942-bec8-f96fa798c341", + "89586b79-902d-5e2b-9b8a-b7a8c4971783", + "94acf45b-980d-5273-8a09-5d748c94a51b", + "e3eb627c-15f4-5713-92a4-e92a891b7136" + ], + "document_id": [ + "4d5b1800-b676-5865-a555-09ea740cc14a", + "24f073af-ef97-5ba3-9923-9a7d958bd411", + "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "dff49223-ac74-5419-a190-a0c7f43a5ee5", + "6d2b82c3-4256-562a-9b23-ff7c71e9fd93", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "2e038219-fdaa-506f-9cd3-51379054130e", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "6277f22c-f56d-51a7-add1-1fe7674dda74" + ], + "id": [ + "chatcmpl-AIFgqiJDPdSbdoRhIXU84YMtAnqaJ", + "91375d45-be1d-5c54-8d0f-a9b1dded69bb", + "a32e8775-583f-5827-a590-b7058b255d26", + "aba78d88-b097-52fe-8246-66301e39cdd5", + "741dc9f2-2e8e-5fe3-9e6f-806a5a93213b", + "0916cf4a-a863-5c5d-b687-2ae5fa80bac0", + "b3e0de69-763f-5f19-aeb7-ea1df79a143b", + "e58a6718-dfef-58f6-9417-4abd793fe74d", + "71eb66cb-130c-5183-ba9e-038637582775", + "a0aa0b47-91a6-5f3e-b8a2-9ccdfcd79865", + "322613d7-921b-5e2e-b410-57ab4acc4130" + ], + "contexts": [ + "for molecular biological studies on aging. Although material from humans should be employed where possible, for prac- tical reasons animal model systems like rats and mice are indispensible. There is evidence that, provided their health sta- tus and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978). For studying certain funda- mental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also", + "Until now most of the genomic studies of invertebrate models have been performed on whole animals. Several studies, however, recently performed on specialized mammalian tissues, either post-mitotic (heart or nervous system) or mitotic (liver), show that the effects of aging are tissue-specific [19-25]. In addition, effects of caloric restriction on age related transcriptional changes are also tissue- or species-specific [19]. To better understand the aging process in invertebrate", + "opportunities for assessing the efcacy of interventions onaging. When considering the advantages and disadvantages of dogs as a model for geroscience research, it is useful tonote that the vast majority of mammalian studies on thebasic biology of aging are performed in a relatively small number of inbred mouse strains. Typical average lifespan for most of these mouse strains is approximately 23 years,", + "[14] Gerstbrein, B., Stamatas, G., Kollias, N., Driscoll, M. In vivo spec- trofluorimetry reveals endogenous biomarkers that report health- span and dietary restriction in Caenorhabditis elegans . Aging Cell 2005 , 4: 127-137. [15] Kennedy, B.K. The genetics of ageing: insight from genome-wide approaches in invertebrate model organisms. J. Intern. Med. 2008 , 263: 142-152. [16] Kenyon, C., Chang, J., Gensch, E., Rudner, A., Tabtiang, R. A C.", + "the DNA level leads to changes in gross phenotype, we must now look downstream at changes in gene expression associ - ated with genetic variation, aging, and ARD. Comparison With Laboratory Models of Aging Laboratory models typically used to study aging, such as Caenorhabditis elegans (nematode worm) and Mus musculus (mice), have drastically shorter life spans than our own (~3 wk [ 51] and ~3 y [ 52], respectively, vs a 122 y maxi - mum for humans thus far; [ 53]). In some respects, these", + "ing studies on invertebrate models of aging, long-lived mam-mals, transgenic mouse strains, and interventional studies, have led to the identification of evolutionarily conserved path- ways involved in life span regulation, as well as common de- nominators of aging in different organisms. 4 In this review, the pathophysiological roles of these aging mechanisms, including oxidative stress, mitochondrial dysfunction, impaired resis-", + "chain triglyceride oil on life span of genetically heterogeneous mice. J. Gerontol. A. Biol. Sci. Med. Sci. 68, 616 (2013). [PubMed: 22451473] 24. Yuan R, Peters LL & Paigen B Mice as a mammalian model for research on the genetics of aging. ILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 52, 415 (2011). 25. Saul MC, Philip VM, Reinholdt LG & Chesler EJ High-diversity mouse populations for complex traits. Trends Genet. 35, 501514 (2019). [PubMed: 31133439]", + "lowing the discovery of genes and pathways involved inanimal lifespan extension, human research has focusedon the corresponding candidate human genes withgenetic, genomic and epigenetic studies into ageingand longevity. The designs of these studies differwith respect to the selection of naturally occurringphenotypes and the study populations, which includepopulation-based, patient-based, family-based andexposure-based cohorts. Studies into human age-related disease phenotypes", + "Animal studies as stalking horses for human biogerontology. For the most part, studies on the biology of aging are as difficult and imprac-tical in humans as are studies of health insurance in rodents. It is fairlyCopyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html", + "review of the evidence for genotype-dependent eects on lifespan. Ageing Res. Rev. 11, 254270. doi: 10.1016/j.arr.2011.12.006 Turturro, A., Witt, W. W., Lewis, S., Hass, B. S., Lipman, R. D., and Hart, R. W. (1999). Growth curves and survival characteristics of the animals used in the biomarkers of aging program. J. Gerontol. Ser. Biol. Sci. Med. Sci 54, B492B501. doi: 10.1093/gerona/54.11.b492 Vertti-Quintero, N., Berger, S., Solvas, X. C. I, Statzer, C., Annis, J., Ruppen," + ], + "answer": "Scientists study the genetics of aging in animals using a variety of approaches and model organisms. Here are some key methods and models mentioned in the context: 1. **Animal Model Systems**: Rodents, such as rats and mice, are indispensable for molecular biological studies on aging due to their practical advantages and similarities in aging processes to humans [1]. 2. **Tissue-Specific Studies**: Recent genomic studies have been performed on specialized mammalian tissues, both post-mitotic (e.g., heart, nervous system) and mitotic (e.g., liver), to understand the tissue-specific effects of aging. These studies also explore how caloric restriction affects age-related transcriptional changes, which are tissue- or species-specific [2]. 3. **Laboratory Models**: Common laboratory models for studying aging include Caenorhabditis elegans (nematode worm) and Mus musculus (mice), which have shorter lifespans than humans, allowing for more rapid observation of aging processes [5]. 4. **Transgenic Mouse Strains and Interventional Studies**: These studies have identified evolutionarily conserved pathways involved in lifespan regulation and common denominators of aging across different organisms [6]. These methods and models help scientists uncover the genetic and molecular mechanisms underlying aging, providing insights that can be applied to understanding human aging and developing potential interventions.", + "question": "How do scientists study the genetics of aging in animals?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_5 new file mode 100644 index 0000000..4e41bb9 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2001 - Demography in the age of genomics.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", + "2013 - Gene expression changes with age in skin.pdf", + "2017 - Genome-wide meta-analysis associates HLA.pdf", + "2012 - Genome-Wide RNAi Longevity Screens in Caenorhabditis elegans.pdf", + "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", + "2017 - Four Genome-Wide Association Studies Identify New.pdf" + ], + "extraction_id": [ + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "0e71cb8b-4647-56bb-9b5f-d14d0ea2ff50", + "ead14808-bfb7-5e32-9830-28efaae71151", + "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", + "18e9f96b-9af8-59bf-9323-084df9a4f9b6", + "c302507d-508e-5093-a877-1cec20f8440e", + "f160f818-03bf-5b4e-b1f4-bfbd3b0bfb99", + "eed176e2-226d-5638-b10c-4813cfbbd213", + "52557433-1cf1-5441-b44d-361963a61eca" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "62b635c3-040e-512a-b016-6ef295308a1e", + "8e452186-a71c-5b62-81b2-7681c87c8e1d", + "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "3a565ba9-ee5b-5596-b870-ce8c055cb1f1", + "7589fec2-e893-5a4d-9f0c-09abb35858ab", + "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", + "c10653f6-b3d7-5b92-9271-ab8fcc7905a7" + ], + "id": [ + "chatcmpl-AIFgxEMof4wOrO9UyQT5bEPH8q8fk", + "9defe0af-80a1-56da-90df-551fd55baa13", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "2f28f34e-bf5e-57d6-8a8c-dd946f574906", + "b3e21ac9-8df8-5119-a769-a9da82db78da", + "c2234f77-2268-57d0-a227-e931fc4802c1", + "cc78a209-081f-5e0c-877a-36b85d5105a6", + "726417dd-f626-5197-966d-6a6ad25ff718", + "300f0303-caec-52b9-852b-8e67cec5d326", + "025a94a9-595e-56f6-8c03-89ccea15a22c", + "68e705e1-54a1-578a-98ee-0c76b02ccf79" + ], + "contexts": [ + "genes analyzed for their possible association with human lon-gevity (http://genomics.senescence.info/genes/longevity.html).All longevity association studies in humans we could find by thetime of the latest update were added to this list. These includestudies reporting negative results, which we see as essentialsince many genes display population-specific associations withlongevity. Fig. 1 From the main page of the Human Ageing", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "Exceptional Longevity One approach to identifying genes associated with low mortality is to examine the genes of those who survive to the oldest ages. Several studieshave examined gene frequencies among centenarians or nonagenariansand compared them with frequencies at younger ages. Since changes ingene frequencies are more rapid when mortality rates are high, cross-sectional comparisons must be adjusted for differences in mortality amongcohorts.", + "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. PLoS Genet. 2015. https://doi.org/10.1371/journal.pgen. 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, etal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:7029. 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. 2017;70:17789.", + "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", + "expression of certain genes have an effect upon longevity. Although similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "4. Joshi, P. K. et al. Variants near CHRNA3/5 and APOE have age- and sex- related effects on human lifespan. Nat. Commun. 7, 11174 (2016). 5. Pilling, L. C. et al. Human longevity is in uenced by many genetic variants: evidence from 75,000 UK Biobank participants. Aging 8, 547560 (2016). 6. Deelen, J. et al. Genome-wide association meta-analysis of human longevity identi es a novel locus conferring survival beyond 90 years of age. Hum. Mol. Genet. 23, 4420 4432 (2014).", + "79-91. [97] Smith, E.D.; Kennedy, B.K.; Kaeberlein, M. Genome-wide identification of conserved longevity genes in yeast and worms . Mech. Ageing Dev. , 2007 , 128(1), 106-11. [98] Chen, D.; Pan, K.Z.; Palter, J.E.; Kapahi, P. Longevity determined by developmental arrest genes in Caenorhabditis elegans. Aging Cell, 2007 , 6(4), 525-33. [99] Curran, S.P.; Ruvkun, G. Lifespan regulation by evolutionarily conserved genes essential for viability . PLoS Genet. , 2007 , 3(4), e56.", + "9. vB Hjelmborg J, Iachine I, Skytthe A, Vaupel JW, McGue M, et al. (2006) Genetic influence on human lifespan and longevity. Hum Genet 119: 312321.doi:10.1007/s00439-006-0144-y. 10. Sebastiani P, Perls TT (2012) The genetics of extreme longevity: lessons from the new England centenarian study. Front Genet 3: 277. doi:10.3389/fgene.2012.00277.11. Perls TT, Wilmoth J, Levenson R, Drinkwater M, Cohen M, et al. (2002) Life-", + "39. Fortney K, Dobriban E, Garagnani P, etal. Genome-wide scan informed by age-related disease identifies loci for exceptional human longevity. PLoS Genet. 2015;11:e1005728. doi:10.1371/journal.pgen.1005728 40. Beekman M, Nederstigt C, Suchiman HE, et al. Genome-wide asso- ciation study (GWAS)-identified disease risk alleles do not compromise human longevity. Proc Natl Acad Sci U S A. 2010;107:1804618049. doi:10.1073/pnas.1003540107" + ], + "answer": "Yes, specific genes have been linked to longer lifespans. Research has identified more than 700 genes that regulate lifespan in model organisms, with many of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, shown to affect longevity across different model organisms [2]. Additionally, variants near genes such as CHRNA3/5 and APOE have been found to have age- and sex-related effects on human lifespan [7]. Genome-wide association studies have also identified loci associated with exceptional human longevity [10].", + "question": "Are there specific genes that have been linked to longer lifespans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_6 new file mode 100644 index 0000000..ad08e52 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2008 - Telomeres and Aging.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2017 - The Aging Cardiovascular System.pdf" + ], + "extraction_id": [ + "396708f1-aa0a-571e-a8d3-7cb8404e9502", + "41b98643-1948-519b-8b27-ab0fa4041048", + "d4afa45a-5efa-577b-822e-7a82c2f6508d", + "55fd2e43-f58e-5d89-8730-7d82d3b6c44f", + "016d8de2-949f-511e-a9e1-d2d5fd2bede5", + "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68", + "5179130e-5fa6-5979-ba68-270e546e43d7", + "9fafad4c-f208-53e0-b2ac-f10569429a5e", + "016d8de2-949f-511e-a9e1-d2d5fd2bede5", + "82798504-5de9-513c-b3df-09968387cd42" + ], + "document_id": [ + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "61d9c326-d36e-55c1-a891-335dc943e70f", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "262df0d6-ad68-544a-88ed-b4568f305858", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c" + ], + "id": [ + "chatcmpl-AIFh26X5nul0obtiAeqSkHmHNgJoq", + "53508a9e-d064-58a3-a4f9-0785470a1462", + "b532d055-ab02-5326-8eb4-67e7277a92b8", + "65fb74aa-f3c3-5c80-919f-329169db982f", + "ab6a6bda-490d-5b7e-a715-3b9b4f89243f", + "80a2162f-6208-5f97-a646-e8803d501f4e", + "f181e6da-58b6-5f26-87a2-355e25388673", + "6d0cccc5-3ed7-507e-9f7a-6035badacc00", + "72b978c7-44fc-530d-a1d2-eaffaf2c8782", + "0faa4fb9-efa7-5e92-8fe4-5e28c51dbee4", + "b1383516-a23e-5048-9cf3-944b5142e16b" + ], + "contexts": [ + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "that shorten their length with progressing age. This shortening of telomeres is the result of the absence of the activity of an enzyme called telomerase, and in turn it induces several processes, such as apoptosis, senescence, or oncogenic transforma- tion of somatic cells, affecting the health and lifespan of an individual [42]. Human telomere shortening has been mostly studied in leukocytes and linked not only to ageing and life expectancy [43] but also to age-related diseases, including cardio-", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "Telomeres play a central role in cell fate and aging by adjusting the cellular response to stress and growth stimulation on thebasis of previous cell divisions and DNA damage. At least a few hundred nucleotides of telomere repeats must cap eachchromosome end to avoid activation of DNA repair pathways. Repair of critically short or uncapped telomeres by telomeraseor recombination is limited in most somatic cells and apoptosis or cellular senescence is triggered when too many uncappedtelomeres accumulate.", + "ing (84). This process is believed to be the trigger for the aging process, according to the telomere theory (11, 85, 86). It is further supported by Bodnar etal. who proved that telomere elongation caused by ectopic expression of telomerase avoids the senescence phenotype (87). His work relied on one of the earliest studies linking telomere shortening to aging which was performed", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of", + "and consequently lose telomeric sequences, thereby limiting the number of cell cycles, which is important for preventing the onset of cancer. Cells perceive critically short telomeres as persistentDNA damage. This activates the DNA damage responses, including cell cycle checkpoints, which ultimately leads to a permanent cell cycle arrest (cellular senescence). Senescence protects from cancer but contributes to the aging process (37).", + "When the telomeres shorten, this loop is no longer able to form and in turn, the epigenetic regulation is changed to activation of the TPE-OLD genes. This happens before the telomeres reach the critical length that causes activation of DDR, thus leading to another earlier possible effect of telomere shortening on aging (138, 139). Interestingly, a following study by Kim etal. showed that one of the TPE-OLD sensitive genes is hTERT, the core reverse transcriptase component of telomerase (140). This is", + "to maintain proliferation potential (94). Cells with mutated telomerase exhibited irregular morphology and short telomeres, but these changes did not cause deadly damage and determinate senescence (95). One hypothesis connects aging to telomere erosion through the transcription of subtelomeric genes. Genes located in subtelomeric regions are affected by transcriptional silencing which was found to change in an age-related manner. Kim et al. (96) found that silencing of genes in subtelomeric", + "evidence implicates telomere shortening in cellularsenescence. Telomeres consist of repetitive nucleotides e q u e n c e s( T T A G G G )a tt h ee n d so fm a m m a l i a nc h r o -mosomes, that preserve chromosome stability andintegrity by preventing deterioration or fusion withneighboring chromosomes (76) (Central Illustration ).JACC VOL. 69, NO. 15, 2017 Paneni et al . APRIL 18, 2017:1952 67 The Aging Cardiovascular System1957" + ], + "answer": "Telomeres affect the aging process in several ways: 1. **Telomere Shortening**: Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unreplicated on the lagging strand [1]. This shortening is a natural part of the aging process and is exacerbated by genotoxic stress, such as oxidative damage [1]. 2. **Cellular Senescence and Apoptosis**: The shortening of telomeres can induce processes such as apoptosis (programmed cell death) and cellular senescence (a state where cells stop dividing), which affect the health and lifespan of an individual [2]. When telomeres become critically short, they trigger a DNA damage response, leading to cellular senescence or apoptosis [4]. 3. **Genome Stability**: Telomeres ensure the stability of the genome and protect chromosomes from incorrect actions by the DNA repair machinery [3]. When telomeres are too short, they can no longer form protective structures, leading to genome instability and potentially contributing to aging [8]. 4. **Cancer Prevention**: Short telomeres limit the number of cell cycles, which is important for preventing the onset of cancer. However, this also contributes to the aging process as cells enter a state of permanent cell cycle arrest (senescence) [7]. 5. **Telomerase Activity**: The enzyme telomerase can maintain telomere length, but its activity varies over the lifespan and between cell types, tissues, and species [1]. In most human somatic cells, telomerase activity is limited, which contributes to telomere shortening and aging [4]. Overall, telomere shortening acts as a biological clock that limits cellular replication, contributing to aging and age-related diseases [6].", + "question": "How do telomeres affect the aging process?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_7 new file mode 100644 index 0000000..7050781 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Caloric restriction and genomic stability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2017 - Mutation and catastrophe in the aging genome.pdf", + "2007 - Decline of nucleotide excision repair capacity in aging.pdf", + "2005 - DNA Repair, Genome Stability.pdf", + "2023 - Genomic Instability Evolutionary Footprints on Human Health.pdf", + "2005 - DNA Repair, Genome Stability.pdf" + ], + "extraction_id": [ + "a563be97-fd42-50ba-8a26-3e1ca3b738db", + "44047f31-85e4-587c-ba58-8c3494fb7d52", + "e3e52327-4a23-5003-b418-dafdcdcae82c", + "b934a2a9-a672-5d65-9d0d-bbc36652a148", + "2b406c50-28e1-5b8c-a39d-a26db15f8aaa", + "eb91e436-a1bb-5d10-b648-07224b9e5bff", + "a0e59df7-6a34-5f03-af2e-82bdc0edacb9", + "5ea2fb27-ddd7-50b4-b318-39ca71f1c7e2", + "57e201b2-a357-5cff-9555-49955299669e", + "67128b6e-9bd6-53fe-b1e7-d0721db8619d" + ], + "document_id": [ + "76c08863-1522-519b-8da6-65a872418fee", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "7ae205a2-e002-5e8b-bbf1-ea96ab599b37", + "c9bb2ba2-a001-5c1b-8be8-d1c184924362", + "e658e73b-2494-5fa3-ae39-9f4933bc037b", + "59dec4a5-f80a-5a82-b55a-b6b1b33b907f", + "e658e73b-2494-5fa3-ae39-9f4933bc037b" + ], + "id": [ + "chatcmpl-AIFhETjzplVDZqcInKYA6bobssz1r", + "566bd0c9-262e-543e-8934-1af5fa9edef5", + "b8c3720d-f697-5d2f-9728-49b7489d6509", + "9180d1c5-31b6-533e-bf2e-4b367dc2097d", + "ca253ce9-4661-5ca2-bf17-3a86ef3eff1d", + "494f865d-a7b6-5978-9b02-d5e628952a9d", + "a1370bf9-13f2-5c98-9d9d-9dfead21ebd7", + "8d2bc107-4d94-5dd8-8f67-b593aecc0478", + "4db748ed-7063-50e5-b42c-cb6fa3ecd9a2", + "4521b426-a67e-51e4-bc63-b6da5fab60cf", + "4c627903-8a25-5db0-8a60-1850a924a27b" + ], + "contexts": [ + "Effect of age on DNA repair Research over the past decades suggest that many steps in DNA metabolism are altered with age in a variety of tissues and animal models (56,57). The relation of DNArepair to aging has been studied by measuring the ability of cells from organisms of various life spans to repair DNA damage and by experiments that have comparedthe ability of cells from young and old organisms to repair DNA damage. Interest was peaked by the original", + "BI87CH14_Niedernhofer ARI 18 May 2018 15:1 SUMMARY POINTS 1. Evolutionarily conserved DNA repair pathways maintain the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms results in accelerated agingand/or cancer. 2. Evidence in humans and model organisms supports the conclusions that with age (a) endogenous sources of genotoxins increase, ( b) DNA repair capacity declines, and (c) levels of DNA damage and mutations increase.", + "Several lines of evidence suggest that DNA repair capacity might decrease with age. However,it should be noted that measuring DNA repair in tissues is challenging and that the validity ofsurrogate markers of repair capacity is not well established. For example, a reduction in expression of DNA repair genes/proteins is not proven to impact DNA repair. Frequently, the reduction in", + "improved DNA repair. Finally, there should be a plausible mechanism by which DNA damage can drive aging. Here, we review the evidence currently supporting each of these predictions. EVIDENCE THAT DNA DAMAGE INCREASES WITH AGE Sources of Damage Increase with Age The free radical theory of aging posits that aging is caused primarily by oxidative damage in- curred by ROS that chemically modify critical cellular biomolecules (13). This theory has evolved", + "All rights reservedKeywords DNA damage, aging, mutations, senescence, DNA damage response, DNA repair Abstract The nuclear genome decays as organisms age. Numerous studies demon- strate that the burden of several classes of DNA lesions is greater in older mammals than in young mammals. More challenging is proving this is acause rather than a consequence of aging. The DNA damage theory of aging, which argues that genomic instability plays a causal role in aging,", + "repaired; otherwise the genome would soon become saturated with damage and life would cease. There is some evidence that DNA damage accumulates with age in some tissues ( Maslov et al., 2013 ), but the exact nature of the damage remains unclear. Indeed, even these low levels of spontaneous DNA damage may represent a steady state due to continu- ous repair and induction of new damage. However, DNA damage can cause certain aging phenotypes by activating cellular responses, such", + "36:1049-1062. 66. Hasty P, Vijg J: Accelerating aging by mouse reverse genetics: a rational approach to understanding longevity. Aging Cell 2004, 3:55-65. 67. Bohr VA: Deficient DNA repair in the human progeroid dis- order, Werner syndrome. Mutat Res 2005, 577:252-259. 68. Nouspikel T, Hanawalt PC: DNA repair in term inally differenti- ated cells. DNA Repair 2002, 1:59-75. 69. Nouspikel T, Hanawalt PC: When parsimony backfires: neglect- ing DNA repair may doom neurons in Alzheimer's disease.", + "DNA repair. In the latterdifficult to arrive at a strict, experimentally useful defini-context, most premature aging syndromes are causedtion of aging. Factors implicated in organismal declineby mutations in genes encoding proteins involved inin genetic models might not play a role in the normalDNA repair ( Karanjawala and Lieber, 2004 ). Accumula-aging processes. A related difficulty is that prematuretion of mutations in critical genes may be one generalaging models fail to recapitulate all aspects of", + "escape the repair process and accumulate in the genome, impacting several processes and aging [67,145147]. There is little evidence of association between DNA repair improvement and life- time expansion [ 148,149], thus, indicating that such mechanism seems to have evolved to maintain DNA stabilityand therefore healthonly until reproductive age, without any regard for the fate of the individual in old age, both in terms of quality and length of", + "with age, and DNA repairtween different tissues. These differences likely reflectdefects can cause phenotypes resembling prematurefunctional characteristics of those tissues, such as mi-aging. We discuss how cellular DNA damage re-totic rate, transcriptional activity, metabolism, and thesponses may contribute to manifestations of aging.action of specific DNA repair systems.We review Sir2, a factor linking genomic stability, me-Reactive Oxygen Species: An Important Sourcetabolism, and aging. We conclude" + ], + "answer": "DNA repair plays a significant role in aging by maintaining the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms can result in accelerated aging and/or cancer [2]. As organisms age, endogenous sources of genotoxins increase, DNA repair capacity declines, and levels of DNA damage and mutations increase [2]. This accumulation of DNA damage is associated with aging phenotypes, as DNA damage can activate cellular responses that contribute to aging [6]. The DNA damage theory of aging suggests that genomic instability, caused by accumulated DNA damage, plays a causal role in aging [5]. Additionally, the burden of DNA lesions is greater in older mammals compared to younger ones, indicating that DNA repair is crucial for mitigating the effects of aging [5].", + "question": "What role does DNA repair play in aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_8 new file mode 100644 index 0000000..26e2861 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2001 - Demography in the age of genomics.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Protecting the Aging Genome.pdf", + "2012 - Peroxiredoxins, gerontogenes linking.pdf", + "2019 - Genomics of 1 million parent lifespans.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2016 - Progress on the role of DNA methylation in aging.pdf" + ], + "extraction_id": [ + "e3014138-3d5b-58bc-a1a5-5ac6f04cac1c", + "68b12e10-d643-570a-aa63-eda62de81928", + "e5067ce2-69a6-5433-bed4-b95daeaa691e", + "38ebdc6a-8e8e-5472-a3ed-9a0f06591474", + "ff0adc7c-70ff-5b14-ba7d-a9dda60fac80", + "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "8650652a-1765-563b-a98e-2e9336bcf29a", + "822571e2-b05d-5e17-9eaa-431151851111", + "b9f038dd-97af-51ea-bb32-d73bf66c3dcb", + "8829c724-73ff-582b-ab94-c9f1a906cfd5" + ], + "document_id": [ + "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "bb774030-2570-5596-b2ab-b8f57ff81086", + "2eaad7ba-b6ae-5382-ba79-84609080b53e", + "f68b939c-847b-5eac-8926-24713ae43478", + "70945353-4808-539a-80f9-5632c27913e5", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "62b635c3-040e-512a-b016-6ef295308a1e", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "e4cdc02f-4415-5638-aab8-f848b4d64a22" + ], + "id": [ + "chatcmpl-AIFhM7HonwMIv1KCdMHKw9gGzAYlV", + "9da7c5dc-0deb-577c-bb22-83f987bd76dd", + "78733c6a-d870-5154-9128-eb66291fa967", + "3c636897-c47e-505d-9203-306124b73e0e", + "43cba086-7f03-529f-bcd0-6483202bf3c7", + "de7c30f6-cce9-563d-83f4-809f2aab781b", + "4eb34c07-921b-55bb-98eb-ff013bb2ace0", + "f20fd517-5f05-53ca-93a5-916bc891ad92", + "265126e3-2a4d-518f-93cf-21a201747eef", + "afc304d1-dd43-55ec-811d-27ca27fc4e5d", + "1c77b8dc-2fd6-5e3d-9cf0-5585e7c9fb57" + ], + "contexts": [ + "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections. Prospective Epidemiologic Surveys that Include Genetic Information Some epidemiologic cohort studies of populations have collected", + "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi- ple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage. Future Prospects: Developing Interventions through DNA Repair", + "and potentially important genetic markers for slow aging have been found in humans (Suh et al. 2008). Elucidating the function of such genes is believed to enable decipher- ing the core of the aging process, answer to what extentthe process is conserved, and pave the way for therapeutic interventions of age-related maladies, including cancers, neurodegeneration, and metabolic syndrome (Guarente 2011). The identity of the virtual gerontogenes so far discov-", + "discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. DOI: https://doi.org/10.7554/eLife.39856.002", + "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in molecular biology and cell culture approaches (for instance Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identication of new potential therapeutic targets. In", + "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705. 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov- ery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190. 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction, appear to extend not only life span, but also health span (Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does appear that it is possible to experimentally slow the rate of aging. Still, in each case, aging does continue on as if there is some", + "genetic modification. Currently, emerging evidence suggeststhat certain interventions (e.g. CR, dietary supplementation andchemical drugs) can prevent age-related diseases and promote longevity, at least in part, through reversing the aberrant age- associated changes in DNA methylation, suggesting the greatpotential of DNA methylation in therapeutic strategies againstage-related diseases ( Figure 1B ).However, to further understand the roles of DNA methyla-" + ], + "answer": "Yes, genetic research can potentially lead to treatments that slow down aging. Several pieces of evidence from the context support this possibility: 1. The discovery of genetic markers for slow aging in humans suggests that understanding these genes could pave the way for therapeutic interventions for age-related maladies, including cancers, neurodegeneration, and metabolic syndrome [4]. 2. Research indicates that manipulating aging-related genes through various means, such as diet, lifestyle, and pharmaceuticals, could dramatically improve human health and lead to the development of drugs against age-related diseases [7]. 3. Advances in molecular biology, such as CRISPR/Cas9, are expected to clarify aging processes and identify new potential therapeutic targets, which could be crucial for developing treatments that slow aging [6]. 4. The use of senolytic drugs, which target senescent cells, has shown promise in halting biological aging in mice, and trials are underway to test their effectiveness in humans [3]. 5. There is a suggestion that interventions targeting DNA methylation and other genetic modifications could prevent age-related diseases and promote longevity, highlighting the potential of genetic research in developing therapeutic strategies against aging [10]. Overall, while the research is still ongoing and some findings are speculative, there is significant potential for genetic research to contribute to treatments that slow down the aging process.", + "question": "Can genetic research lead to treatments that slow down aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_9 new file mode 100644 index 0000000..799cb0c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_aging_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Sex Differences in Aging Genomic Instability.pdf", + "2017 - Independent impacts of aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "2004 - Ageing, repetitive genomes and DNA.pdf", + "1999 - Molecular Biology of Aging.pdf", + "1998 - Neurodegeneration and Aging Role.pdf" + ], + "extraction_id": [ + "400784cf-bb7d-5bf8-b735-2142ebf7c953", + "1f0b6363-a045-53aa-a124-4cf89e61fc26", + "c8db1d28-f6c2-5896-95ec-bb01159ba483", + "385c192b-a416-5208-9615-20111ce782aa", + "381cc064-9970-5dcd-b959-c52a8e487fe7", + "ef9463cd-cf21-527f-ae4a-3df211c78435", + "5cbace8d-e538-5531-9311-ea9726ad2f15", + "9b7b2005-857b-5379-ba5c-3a8c6fd6c891", + "68c55aac-d8fa-5287-a420-2bb83a2c159e", + "21db434d-3cf5-5ff1-8257-0941ebe74822" + ], + "document_id": [ + "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "d1d0b9ce-f827-5dfb-8e39-d87a9ca52f6d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "62b635c3-040e-512a-b016-6ef295308a1e", + "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "1772d596-16a3-547a-9f76-2cb658e89746", + "6f122d3a-d8ca-598f-8767-c059a941cef3", + "694c44f3-6025-5a2c-9c72-d9c5f16c8b85" + ], + "id": [ + "chatcmpl-AIFhRRoCeBcURczVYxgfGYsJd8loh", + "9ec5a15f-8232-5e79-a78d-64eeba35747f", + "39019881-9b6d-5111-87ea-71c413bdf4ff", + "1a9d5c26-f606-5cb5-98ee-4120de3fbd1a", + "e0d41918-20fb-53f0-ac63-cd079c6dce1e", + "ef3be4e1-4cbc-5b61-a286-9b759df08cd9", + "4206977e-23df-5307-8d8a-cb2ed7b33595", + "7095cdbb-852e-541e-884b-a9e67c2c790c", + "512e09e3-c880-5bed-9071-bfa84d80a5a3", + "2e2de9a7-2e83-5f46-a4b7-08eddcd37baa", + "c35761f9-58e8-526b-94fd-96ed7599cb53" + ], + "contexts": [ + "In addition to nuclear DNA, mitochondrial DNA (mtDNA) also is affected by aging. Alterations in mitochondrial function and mito-chondrial-nuclear signaling occur during aging and have been linked to sex biases in aging and age-related diseases (28). Due to their role in energy production, mitochondria are at high risk of oxida-tive damage. Not surprisingly, accumulation of oxidative lesions is an important source of age-related mtDNA damage (29). In aged Wistar rats brains, DNA oxidation, as measured by", + "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569. 20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G, Freyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature. 2013;501(7467):412 5. 21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20(8):1653 9.", + "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/ s12864-017-4287-0. 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema- topoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "other studies, the risk for metabolic disorders is highly associated with age-related diseases that affect lifespan, and interestingly these conditions exhibit mitochon- drial dysfunction [73]. Aging is a complex process as a time-dependent progressive loss of physiologi- cal integrity, leading to impaired function and increased vulnerability to death [74], and as we described above, aging is highly associated with mtDNA mutations; in", + "mt, and overall mitonuclear genomic compatibility. Given the uncertainty of mtDNA mutation accumulation in driving the natural aging process, it is plausible that mito - chondrial communication may be a significant evolutionarily conserved force that influences lifespan and/or healthspan. Acknowledgements Funding was provided by the American Federa- tion for Aging Research (AFAR), the National Institute on Aging (T32", + "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. 2014;306:E58191. 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial function in aging. Mech Ageing Dev. 2010;131:45162. 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging. Mol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028 Symer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G. and Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic instability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5 Szabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "than ones that affect mitochondrial DNA12,57,58,71.So,this is an important reason for favouring nuclear DNA as the ultimate damage target in natural ageing. Nevertheless, it is conceivable that when mutations occur in the mitochondrial genome, mutant-protein production could increase the inefficiency of the mitochondrial respiratory chain, thereby resulting in more reactive oxygenspecies, which would then damage nuclear and mitochondrial DNA further.", + "generation animals as they grow older.Mitochondrial DNAGenetic instability outside of the nuclear genome mightalso contribute to aging (reviewed in Lee et al., 1997;Wallace et al., 1998). The mutation rate for mitochondrialDNA (mtDNA) is 10- to 20-fold greater than for nuclearDNA, and it is believed that mtDNA mutations may com-promise mitochondrial functions in different ways (Fig-ure 4). First, defects in electron transport and oxidativephosphorylation could lead to declines in ATP levelsand the NAD:NADH", + "of the human aging process(Corral-Debrinski et al., 1992; Soong et al., 1992;Wei etal., 1996b), and it has been demonstrated that certain pointmutations of mitochondrial DNA accumulate in the aginghuman brain (Zhang et al., 1993; Liu et al., 1997). However,thefunctionalimplicationsofthesendingsarecontroversial(Hayashietal.,1994).Tocomplicatethematterfurther,Takaiand co-workers discuss the possibility that the commonage-associated changes in human and mouse" + ], + "answer": "Mitochondrial DNA (mtDNA) influences aging through several mechanisms: 1. **Oxidative Damage**: Mitochondria are crucial for energy production and are highly susceptible to oxidative damage. The accumulation of oxidative lesions in mtDNA is a significant source of age-related damage [1]. 2. **Mutations and Lifespan**: Mutations in mtDNA can reduce lifespan. These mutations can aggravate aging and impair brain development, indicating a direct link between mtDNA mutations and the aging process [2]. 3. **Mitochondrial Dysfunction**: Aging is associated with mtDNA mutations, which contribute to mitochondrial dysfunction. This dysfunction is linked to age-related diseases and metabolic disorders, further influencing lifespan [4]. 4. **Genetic Instability**: The mutation rate for mtDNA is significantly higher than for nuclear DNA. These mutations can compromise mitochondrial functions, such as electron transport and oxidative phosphorylation, leading to declines in ATP levels and increased production of reactive oxygen species, which further damage both nuclear and mitochondrial DNA [9]. Overall, mtDNA influences aging by accumulating mutations and oxidative damage, leading to mitochondrial dysfunction and contributing to age-related physiological decline.", + "question": "How does mitochondrial DNA influence aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_1 new file mode 100644 index 0000000..edbcd4d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2019 - How Recent Advances in Genomics Improve Precision Diagnosis.pdf", + "1994 - Isolation of the Human LIMTHomeodomain Gene Islet-1.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2004 - Diabetes Genes a.pdf", + "1986 - Diabetes due to secretion of a structurally abnormal insulin.pdf", + "2004 - Diabetes Genes a.pdf", + "2001 - Genomic variation in pancreatic ion channel genes in Japanese type 2 diabetic patients.pdf", + "2011 - Genome-wide association studies (GWAS) impact.pdf", + "2019 - The clinical and genetic characteristics of permanent neonatal diabetes PNDM in the state of Qatar.pdf" + ], + "extraction_id": [ + "7d87ee73-2cc1-576c-8c0d-eb58479177b8", + "ebb48c39-f48a-5dce-a4dc-fcd34e6f17e1", + "b3b6cf5d-8cc3-5559-af5f-36780a303792", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "ce0307a5-fae4-5b6d-9786-10619e49e075", + "ffdee7b8-ff45-57bf-973a-ca03ba595d23", + "508fd29c-5cf8-52bc-8bf2-5cebb1833cb1", + "2d845f58-a674-5e08-a68b-e8cfea8a9b84", + "a4721e1b-273b-5042-bc20-cf5602f0955a", + "d340209d-0773-5cfc-b0d1-e714f2ebe9f2" + ], + "document_id": [ + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "42b64375-06af-5e09-9ae6-6bd0ecb782c7", + "c77b101b-9f78-5090-8be9-6f98d9380466", + "eee2f79d-e093-52fb-871a-798fd859235e", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "5b884835-4cf4-5e80-a762-36582271e63e", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "bff7795f-c109-5d7f-871d-ef1f4400a2c6", + "086c6869-7c70-5364-9269-760267fb458d", + "90268a68-3926-535e-952e-735e206eb3ab" + ], + "id": [ + "chatcmpl-AIFp3LBsYqGcLUvIYyVnKoJE32qw3", + "111f4875-7ccd-502f-bd5b-5d4ee88e5af6", + "3f7cc31a-dc57-568a-a3b0-602280c56428", + "3cb068b3-0761-5fd8-91bd-92744fa9ca9a", + "5d64fd7b-32d2-55ac-8586-c7ba9172a9ef", + "551f3603-6a4c-51e6-b568-7ae2fc9e7b33", + "2126e367-c1aa-56ae-aff4-0ba7e7070a22", + "487ca988-cce2-5b92-a05f-2e1cd11efea3", + "fa07c029-ad6e-5768-97da-a4bc5aa4e44f", + "644810c4-af08-5c60-b333-8c97ddadae8b", + "ac0df77e-c676-552b-b742-1591cb18fbbb" + ], + "contexts": [ + "Mutations that result in mutant insulin or the inability to convert proinsulin to insulin result in gl ucose intolerance in some of these cases. Genetic defects in the insulin receptor or in the signal transduction pathway of insulin have been demonstrated to result in hyperinsulinemia and modest hyperglycemia to severe diabetes[1]. Disease of the exocrine pancreas Damage of the cells of the pancreas due to diffused injury of the pancreas can cause diabetes. This damage", + "A, et al. Insulin gene mutations resulting in early-onset diabetes: marked differences in clinical presentation, metabolic status, and pathogenic effect through endoplasmic reticulum retention. Diabetes. 2010;59:653 61. 21. Steele AM, Shields BM, Wensley KJ, Colclough K, Ellard S, Hattersley AT. Prevalence of vascular complications among pa- tients with glucokinase mutations and prolonged, mild hyperglyce- mia. JAMA. 2014;311:279 86.22. Chakera AJ, Spyer G, Vincent N, Ellard S, Hattersley AT, Dunne FP.", + "presumed glucose toxicity (34). The finding that a mutation of a single nucleotide in the gene encoding the glucokinase enzyme can result in NIDDM lends credibility to the hypoth- esis that inherited defects in insulin production contribute to NIDDM (6). Increased insulin demand of obesity and insulin resistance is accompanied by enhanced insulin biosynthesis,", + "insulin synthesis and function while mutations in the insulin gene ( INS) obviously affect the key hormone made by pancreatic beta cells [62]. ATP synthesis defect (mitochondrial diabetes) and mutations in ATP- sensitive potassium channel subunits (channel-building Kir6.2 [po- tassium inwardly-rectifying channel, subfamily J, member 11;KCNJ11 ] and regulatory SUR1 [ATP-binding cassette transporter subfamily C member 8], ABCC8 ) all affect insulin secretion [63].", + "Insulin gene mutations Insulin is synthesized in 13-cells of the islets of Langerhans and is a central honnone that maintains glucose homeostasis. Insulin-deficient mice die shortly after birth due to severe hyperglycemia.53 All cell types of the endocrine pancreas are present in insulin deficient mice suggesting that insulin is not required for development and differentiation of the endocrine pancreas. 53 Naturally occurring mutations in the insulin gene that result in the", + "Theprevalenceofgeneticmutationsaffectingthestructure oftheinsulinmoleculeinthegeneralpopulationisunknown. Uptothepresent,onlythosepatientsmanifestingthemutant insulinsyndrome(5-8,36)withunusualorfamilialTypeII diabeteshavebeenscreenedanddiscovered.Thus,mutantin- sulinspecieswithnormalorrelativelywell-preservedbinding andbiologicalactivitycharacteristics,andthereforenormal metabolicclearances,areunlikelytobediscoveredbythisap- proachsincehyperinsulinemiawillbeabsentorsubtle.Future", + "at various steps, resulting in an impaired insulin action and potential development of extreme insulin resistant clinical conditions. Many mutations have been identified in the insulin receptor gene. These mutations may lead to: Decreased insulin receptor biosynthesis Premature chain termination in extracellular or intracellular domain Accelerated receptor degradation Defect in the receptor transport to plasma membranes Decreased insulin binding affinity Impaired tyrosine kinase activity", + "15. Steiner DF, Tager HS, Chan SJ, et al . Lessons learned from molecular biology of insulin-gene mutations. Diabetes Care 1990; 13: 600609. 16. Vionnet N, Stoffel M, Takeda J, et al . Nonsense mutation in the glucokinase gene causes early-onset non-insulin-dependent diabetes mellitus. Nature 1992; 356 : 721722. 17. Sakagashira S, Sanke T, Hanabusa T, et al . Missense mutation of amylin gene (S20G) in Japanese NIDDM patients. Diabetes 1996; 45: 12791281.", + "vating mutations in the gene encoding Kir6.2 alter fetal and postnatal growthand also cause neonatal diabetes. J Clin Endocrinol Metab 2006; 91(7): 27822788. 93. Stoy J, Edghill EL, Flanagan SE, et al. Insulin gene mutations as a cause of permanent neonatal diabetes. Proc Natl Acad Sci U S A 2007; 104(38): 1504015044. 94. Pulizzi N, Lyssenko V, Jonsson A, et al. Interaction between prenatal growth and high-risk genotypes in the devel-opment of type 2 diabetes. Diabetolo- gia2009; 52(5): 825829.", + "(Edghill et al., 2008; Garin et al., 2010; Stoy et al., 2007). Hyperglycemia occurs due to decreased insulin biosynthe-sis, in which most of the reported missense heterozygous mutations are expected to cause an abnormal proinsulin folding. An accumulation of the misfolded protein in the en-doplasmic reticulum (ER) consequently occurs, resulting in ER stress and betacell apoptosis (Liu, Hodish, Rhodes, & Arvan, 2007). Our identified de novo novel variant in INS is expected to result in aberrant proinsulin" + ], + "answer": "Genetic mutations in the insulin gene can significantly affect glucose metabolism in several ways: 1. Mutations that result in mutant insulin or the inability to convert proinsulin to insulin can lead to glucose intolerance. This is because the proper conversion and function of insulin are crucial for maintaining normal glucose levels in the blood [1]. 2. Insulin gene mutations can cause early-onset diabetes, with marked differences in clinical presentation and metabolic status. These mutations can lead to the retention of insulin in the endoplasmic reticulum, causing stress and potentially leading to beta-cell apoptosis, which reduces insulin production and secretion [2], [10]. 3. Insulin-deficient conditions, such as those caused by certain mutations, can lead to severe hyperglycemia, as insulin is a central hormone for maintaining glucose homeostasis. Without sufficient insulin, glucose cannot be effectively taken up by cells, leading to elevated blood glucose levels [5]. 4. Some mutations may result in abnormal proinsulin folding, leading to an accumulation of misfolded proteins in the endoplasmic reticulum. This accumulation can cause endoplasmic reticulum stress and beta-cell apoptosis, further impairing insulin production and secretion [10]. Overall, genetic mutations in the insulin gene can disrupt normal insulin synthesis and function, leading to impaired glucose metabolism and conditions such as diabetes.", + "question": "How do genetic mutations in the insulin gene affect glucose metabolism?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_10 new file mode 100644 index 0000000..4eb17e2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "2019 - How Recent Advances in Genomics Improve Precision Diagnosis.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "2015 - Rare and Common Genetic Events in Type 2 Diabetes.pdf", + "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "6c0f6484-fb94-5583-8c4a-f707983ff29f", + "6c0f6484-fb94-5583-8c4a-f707983ff29f", + "c5c209f5-e4dc-5eb2-a9f0-536a686efa96", + "7117f141-8841-5c11-ba1a-85039181b393", + "4668a316-6c87-5039-b55b-47fe0b8fbc71", + "3c09962c-226d-5271-a5c8-14f6327a079b", + "039d8c39-8fe3-5183-b952-3e8a25510b86", + "587c2476-a12a-5e32-b0cf-ada54ee4a1db", + "318e6c28-16d2-5d59-b047-cfdc6b2e7301", + "d4fcf425-4ac9-5918-973a-fe6c422b19bc" + ], + "document_id": [ + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "076f1140-8992-536f-832b-65ebdb9232a2", + "42b64375-06af-5e09-9ae6-6bd0ecb782c7", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "076f1140-8992-536f-832b-65ebdb9232a2", + "076f1140-8992-536f-832b-65ebdb9232a2", + "641771c3-048c-5afb-a41c-05a883e99408", + "6d341cd2-ae56-5807-9aff-39298efc4d06", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-AIFqHVwVjpSqrvqjfDgFColFbW31Q", + "7d0ed0e8-6967-5dbe-b57e-f1e3a5821fd0", + "e3ec46a7-b0c9-567d-ba4a-e1c4f1341364", + "72000563-bea1-562e-b5d6-ea2c82f74d53", + "b1c43f5d-53c4-58e5-ac10-a90ecdd9d576", + "1555d1c2-53e4-5f7f-8411-7bb11d990eed", + "25e3417d-4e7e-595c-bec6-6f6e3d697ab4", + "e479acca-9418-552b-98ae-edb6eb74ee6f", + "b964fb31-cf7f-5d5d-9d73-d737daa96b8d", + "847efd79-3919-5ec0-b5b3-9934cdb29c39", + "77d42dce-1bb6-577f-95f4-f8c7ece85c19" + ], + "contexts": [ + "studying the highly familial MODY form of young - onset diabetes or other rare forms of monogenic diabetes. Table 12.2 The different subtypes of maturity - onset diabetes of the young ( MODY ). MODY type Gene locus Gene name Year of discovery Distribution Onset of diabetes Primary defect Severity of diabetes Complications OMIM MODY1 20q HNF4A ( TCF14 ) 1996 Rare (2 3%) Adolescence/", + "penetrance and early - onset diabetes, allows the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies. MODY usually develops in thin young adults (usually before 25 years of age; in childhood, adolescence or young adulthood), and is associated with primary insulin - secretion defects [4,5] . The prevalence of MODY is estimated to be less than 1 2% of patients with T2DM, although it could represent as many as 5% of European cases of diabetes [4,25] . MODY is not", + "[2] . Mutations in 13 genes are known to cause MODY; the most prevalent are HNF1A , GCK and HNF4A [3, 4] . The MODY subtypes differ in age of onset of diabetes, the pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifesta-tions [5] . As compared to type 2 diabetes, the clinical Key Words Best practice Genetic testing Healthcare providers Interview study Maturity onset diabetes of the young Abstract", + "causal for MODY , although genetic or functional evidence of obvious pathogenicity is not fully compelling (Table 1). Despite these important advances in understanding the mo- lecular pathogenesis of MODY , the genetic determinants in many patients with young-onset diabetes resembling a MODY-like phenotype remain unknown, suggesting addi- tional locus heterogeneity and new pathogenic mechanismsto be yet discovered. This has particularly been observed in", + "MODY Maturity Onset Diabetes of the Young. This is an uncommon form of diabetes, inherited as an autosomal dominant condition, and displaysa slow onset of symptoms. It generally presents before 25 years of age, is not related to obesity, and appears to have no autoi mmune basis. Multiple forms of MODY have been characterised based on mutations affecting different genes involved in the control of -cellfunction, and display different degrees of disease severity Continued over page", + "Genetic Testing for MODY Public Health Genomics 2015;18:5259 DOI: 10.1159/00036796359 1 Singh R, Pearson ER: The importance of mak- ing a genetic diagnosis of diabetes. Can J Dia-betes 2006; 30: 183190. 2 Ledermann HM: Is maturity onset diabetes at young age (MODY) more common in Europe than previously assumed? Lancet 1995; 345: 648.", + "Genetic Testing for MODY Public Health Genomics 2015;18:5259 DOI: 10.1159/00036796353symptoms present often at a relatively young age in pa- tients without overweight, who have a positive family his-tory. As compared to type 1 diabetes, progression may be less severe, and the required dosage of insulin low. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or 2 diabetes mellitus [4] . In", + "in 1992, through familial linkage analysis of French pedigreeswith early-onset, non-auto-immune, non-obese diabetes thatwas also called maturity-onset diabetes of the young (MODY) (Froguel et al., 1992 ). Mutations in GCK (encoding glucokinase) were shown to cause a relatively benign form of MODY. Inciden-tally, it was the rst time that the direct causative effect of rela- tive insulin deciency was demonstrated in T2D, when insulin", + "gene studies were under powered. However, studies of monogenic forms of diabetes, specifically maturity onset diabetes of the young 2 (MODY2), provided some of the first insights into the contribution of genetic variation to hyperglycemia observed during pregnancy and fetal outcomes. MODY2 is an autosomal dominant form of MODY due to mutations in glucokinase ( GCK ) [2527]. Table 1. Characteristics and treatment modalities of different forms of diabetes mellitus Characteristics Treatment modalities", + "is variable, underlining that this disorder is genetically heterogeneous. Table 1. Definition of MODY Impaired glucose tolerance Age of onset <25 years Autosomal-dominant inheritance Using genetic linkage and candidate gene approaches, mutations in genes on chromosomes 2, 7, 12, 13, 19, and 20 have been linked to MODY and collectively may represent up to 3% of all patients with type 2 diabetes (Table 2). The gene on chromosome 7 (MODY2) encodes the glycolytic" + ], + "answer": "Studying the genetic basis of MODY (Maturity Onset Diabetes of the Young) has provided several important insights: 1. **Genetic Heterogeneity**: MODY is caused by mutations in multiple genes, with at least 13 known genes implicated. The most prevalent mutations occur in the genes HNF1A, GCK, and HNF4A [3]. This genetic diversity leads to different subtypes of MODY, each with distinct clinical characteristics such as age of onset, pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifestations [3]. 2. **Inheritance Pattern**: MODY is inherited in an autosomal dominant manner, which means that it can be passed down through families. This inheritance pattern allows for the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies [2]. 3. **Clinical Presentation**: MODY typically presents in young adults, often before the age of 25, and is characterized by primary insulin secretion defects. It is not related to obesity or autoimmune processes, distinguishing it from other forms of diabetes like type 1 and type 2 diabetes [5]. 4. **Pathogenic Mechanisms**: Despite advances in understanding the molecular pathogenesis of MODY, there remain unknown genetic determinants in many patients with a MODY-like phenotype, suggesting additional locus heterogeneity and new pathogenic mechanisms yet to be discovered [4]. 5. **Impact on Treatment and Diagnosis**: Genetic testing for MODY can lead to more accurate diagnoses and tailored treatment plans. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or type 2 diabetes, highlighting the importance of genetic testing in identifying this condition [7]. These insights underscore the complexity and variability of MODY, as well as the importance of genetic research in improving diagnosis and treatment strategies for this form of diabetes.", + "question": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_11 new file mode 100644 index 0000000..55d5138 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2010 - Diabetes in Asia.pdf", + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2003 - Genome-wide screen in obese pedigrees with type 2 diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf" + ], + "extraction_id": [ + "d7bd898b-1d46-557a-b065-f94fc5310b2a", + "73e1aaff-7ef6-5ca2-9c94-23f5674a4f88", + "2643b341-8c50-5cea-af36-86a8b070a80e", + "11faf4fe-7b71-562e-9901-c428ab20b285", + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "11faf4fe-7b71-562e-9901-c428ab20b285", + "1110f7b4-ab5a-5b41-b37d-a992b29cb20c", + "e99fe157-eda9-5e56-9ec9-8f428de2a161", + "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "f6de8981-a79b-5817-b688-a20f76bff86c" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "ffeebaf9-ff76-5751-9b8b-7a2a4a4f1dc3", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "335a3c08-14d3-5511-ab84-340e64c6f993", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "3b301dd1-17bd-5632-9a96-d6294c6d7650" + ], + "id": [ + "chatcmpl-AIFqUmWTKdcimZ6Y2TYtW6SieUkPG", + "47e8bd94-fd61-57f2-b1d0-cc139d71936a", + "437a7129-63b8-5f34-8273-2eef9535e987", + "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "461b6f32-4dd8-5dc1-b69f-134f949fc021", + "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "78d81651-7215-596a-b128-37e429dc7edb", + "b0d3a09d-36a3-5c6e-a110-3fccddaa74b7", + "e6c0f12d-8136-5a16-b77c-88dd17c3a212", + "d632d486-4e04-5c2d-9cf0-9d614453cab3", + "e1ba568f-cc08-549a-9c87-a23285c3b5dc" + ], + "contexts": [ + "of Diabetes Results of several genome-wide association stud- ies (GWAS) have linked the following common gene variants with a 1520% increased risk of diabetes: reduced insulin secretion via reduce beta-cell mass (CDKAL1, CDKN2A, CDKN2B) and beta-cell dysfunction (MTNR1B, TCF7L2, KCNJ11) and increased insulin resistance related to obesity (FTO) and unrelated to obesity (IRS1, PPARG) [ 11 ]. While most of the early studies", + "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. 46. Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; Charpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al. A rare mutation in ABCC8/SUR1 leading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated with type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "CDKAL1 in uences insulin response and risk of type 2 diabetes. Nat Genet 2007; 39: 77075. 69 Wu Y , Li H, Loos RJ, et al. Common variants in CDKAL1, CDKN2A/ B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008; 57: 283442. 70 Sandhu MS, Weedon MN, Fawcett KA, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet 2007; 39: 95153.", + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 . 74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102. 75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. 76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3]. Obesity is the greatest risk factor for type 2 diabetes mellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF release, free fatty acids, etc.). Both", + "50 most cases of type 2 diabetes are thought to be due to genetic variations that are more common but exert less e ect. In early studies, genetic variants in the peroxisome proliferator-activated receptor- gene (PPARG) 51 and the ATP-sensitive potassium channel Kir62 (KCNJ11) were reproducibly associated with type 2 diabetes. 52 In Asian populations, the protective e ect of the PPARG*A12Ala allele on insulin resistance and risk of type 2 diabetes was not consistently seen. 53", + "49. Cornelis MC, Qi L, Zhang C, et al. Joint e ects of common genetic variants on the risk for type 2 diabetes in U.S. men and women ofEuropean ancestry. Ann Intern Med . 2009;150:541 550(in eng). 50. Hu C, Zhang R, Wang C, et al. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8are associated with type 2 diabetes in a Chinese population. PLoS One. 2009;4:e7643 (in eng). 51. Lin X, Song K, Lim N, et al. Risk prediction of prevalent diabetes in", + "46. Sladek R, Rocheleau G, Rung J et al (2007) A genome-wide asso- ciation study identifies novel risk loci for type 2 diabetes. Nature 445:881 885 47. Lauenborg J, Grarup N, Damm P et al (2009) Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab 94:145 150 48. Florez JC, Jablonski KA, Bayley N et al (2006) TCF7L2 polymor- phisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med 355:241 250" + ], + "answer": "The genes most commonly associated with an increased risk of developing diabetes, particularly type 2 diabetes, include: 1. **CDKAL1, CDKN2A, CDKN2B** - These genes are linked to reduced insulin secretion via reduced beta-cell mass [1]. 2. **MTNR1B, TCF7L2, KCNJ11** - These genes are associated with beta-cell dysfunction [1]. 3. **FTO** - This gene is related to increased insulin resistance associated with obesity [1]. 4. **IRS1, PPARG** - These genes are related to increased insulin resistance unrelated to obesity [1]. 5. **IGF2BP2, HHEX, SLC30A8, WFS1** - These genes have been shown to increase susceptibility to type 2 diabetes in reproducible studies [3]. 6. **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** - These are additional variants identified in a recent meta-analysis as being associated with type 2 diabetes [3]. 7. **KCNQ1** - This gene is associated with susceptibility to type 2 diabetes in East Asian and European populations [6]. These genes have been identified through various genome-wide association studies (GWAS) and other genetic research efforts.", + "question": "What genes are most commonly associated with an increased risk of developing diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_12 new file mode 100644 index 0000000..ba90dbe --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2016 - The Application of Genomics.pdf", + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2008 - Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes.pdf", + "2009 - Genetics of Type 1A Diabetes.pdf", + "2010 - Cardiovascular Disease Risk Factors, Type 2 Diabetes Mellitus, and the Framingham Heart Study.pdf", + "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf" + ], + "extraction_id": [ + "9c49d40d-91d3-5f0d-8eaa-b3efa49ac200", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "7aa2ab48-620b-5b30-b2de-103e103579ba", + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "ba3abde6-0fac-587f-976e-bd0e08c48ae3", + "e9c258eb-26f2-5e33-87a2-7ac5a5b29989", + "e0f816e4-3c97-575e-8bbe-0e006c8c8e61", + "d3fa98dd-b7be-5192-9a7c-71742b1b05e4", + "5763fc63-1abb-5baf-b2ed-ad1b019bdb56", + "aafcb80d-7069-59da-8a21-d6a32f1a8820" + ], + "document_id": [ + "cf8ec75c-8ffe-5baa-830d-ac7a4a5964bd", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "2ec5c9c1-fe53-59ca-b36f-d360dfce0da5", + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "8c66aca1-d4ba-534d-a037-4273de340ee1", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "fb502e5b-7094-58aa-9508-103476a9c035", + "7a98f456-6c43-5e9e-b404-31122159eab8", + "134c506f-f66f-5a17-9e81-1f4c5923fe91", + "b2665466-da66-59f0-8581-a68131e924bf" + ], + "id": [ + "chatcmpl-AIFqbEtJpAtIpQor3Q5twvd1eoH61", + "28d6dfa3-28eb-537b-ad53-7d312f20fc88", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "71ebe60b-4807-5b6f-887a-2ab897a46039", + "6cf756f6-bc3a-515a-a879-7270f663c516", + "59b0a653-0d03-582e-8fb5-009af723b984", + "9d44b00e-027f-557f-a851-e870605ea20f", + "f0ca71ce-f2bb-54f2-a933-dc9c952f1eb8", + "e32de26a-7ad6-51a9-860e-5df0b45d981d", + "b677fe54-5f7e-5d87-a16d-6694578c6f2b", + "530788ae-3a97-50d6-ad96-5463a3dc75e8" + ], + "contexts": [ + "genetic knowledge beyond its use for predic-tion of the individuals type 2 diabetes risk?One major advantage of knowing an at-riskpersons genotype could be to offer an individ-ually tailored lifestyle intervention program to prevent or, at least, to significantly retard the", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "(35). If genetic tests are not helpful in the prediction and prevention of diabetes,they could have a role in discriminatingbetween type 1 and type 2 diabetes. Theepidemic of obesity (36) has made it moredifcult to distinguish diabetes type be- cause many children and young adultswith type 1 diabetes are also obese (37).Misclassi cation poses signi cant risks; an incorrect diagnosis of type 2 diabetes", + "geted at specific genetic mutations, it is likely that accompa-nying diagnostic tests for biomarkers will also become available to confirm whether the target biomarker is present. Genomic Analyses for Diabetes Risk", + "genes improves prediction of type 1 diabetes[published correction appears in Diabetologia. 2015; 58(1):206]. Diabetologia . 2014; 57(12):2521 2529. 57. Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, Hattersley AT, Weedon MN. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults.Diabetes Care . 2016; 39(3):337 344. 58. Redondo MJ, Oram RA, Steck AK. Genetic risk", + "10.2337/db13-1663. 20. Vassy JL, et al. A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA study. Diabetologia. 2012;55:26042612. doi: 10.1007/s00125-012-2637-7. 21. Vassy JL, et al. Is genetic testing useful to predict type 2 diabe-tes? Best Pract Res Clin Endocrinol Metab. 2012;26:189201. doi: 10.1016/j.beem.2011.09.002. 22. Khera AV, et al. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease. bioRxiv. 2017. doi: 10.1101/218388.", + "Genotype Score for Prediction of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2209Type 2 diabetes mellitus is a m ajor health problem worldwide.1 Fortunately, its development can be prevented in many instances,2 and persons at risk can be readily identified with the measurement of a few com - mon risk factors.3-5 Type 2 diabetes is heritable, with a risk for people with familial diabetes as compared with those without familial diabetes that is increased by a factor of 2 to 6.", + "risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets. As germ-line factors, genetic risk variants are present and amenable to study at all times be -", + "offers the opportunity to test whetherknowledge of these genetic loci canimprove our ability to detect who willultimately develop diabetes. To answerthis question, we genotyped 18 well-validated single nucleotide polymorph-isms that had previously been associat- ed with diabetes in large genetics", + "Comprehension of Genomic Risk for Diabetes Public Health Genomics 2014;17:95104 DOI: 10.1159/000358413101their results in-person from a genetic counselor were able to correctly indicate their genomic or lifetime risk score for T2DM and interpret their genomic risk, compared to 50% of participants receiving their results online. This finding aligns with reports that suggest genetic counsel-ing (though limited to reporting of test results in this study) improves patients accuracy of risk perception" + ], + "answer": "Genetic testing can help predict a person's risk for diabetes in several ways: 1. **Tailored Interventions**: Knowing an individual's genotype can allow for the development of personalized lifestyle intervention programs aimed at preventing or significantly delaying the onset of type 2 diabetes [1]. 2. **Role of Genetic Factors**: Genetic factors play a role in determining an individual's risk of developing diabetes, suggesting that genetic testing can help identify those at higher risk [2]. 3. **Genetic Risk Scores**: A genotype risk score can predict type 2 diabetes from a young age, as demonstrated in studies like the CARDIA study [6]. This score can help identify individuals who are at increased risk due to their genetic makeup. 4. **Heritability and Risk Assessment**: Type 2 diabetes is heritable, and genetic testing can help identify individuals with a familial risk, which is increased by a factor of 2 to 6 compared to those without familial diabetes [7]. 5. **Improved Prediction and Stratification**: Genetic testing offers the potential for improved prediction and stratification of patients according to their risk, which can aid in selecting possible therapeutic targets [8]. 6. **Identification of Genetic Variants**: By genotyping specific single nucleotide polymorphisms (SNPs) associated with diabetes, genetic testing can improve the ability to detect who will ultimately develop the disease [9]. Overall, genetic testing provides valuable insights into an individual's risk for diabetes, enabling more targeted prevention and management strategies.", + "question": "How can genetic testing help predict a person's risk for diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_13 new file mode 100644 index 0000000..756256e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2010 - Family History of Diabetes and Prevalence.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes a.pdf", + "2005 - Pathogenesis of Type 2 Diabetes Mellitus.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf", + "2004 - Diabetes Genes a.pdf", + "2010 - Diabetes in Asia.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "b91922c6-7b5b-5fa1-a740-4564ec4cfa36", + "5ae0e120-7064-5ced-84ff-e74fb0f90047", + "40d292c1-03bc-5780-a2ae-9b0fe245f39c", + "8e5322e6-a8a2-5d98-b87d-1ba3846d5fe1", + "d62a1716-bd6a-5532-ab22-ee6e7ec4cf37", + "f6b9d6b9-a60b-56f5-9727-d90d43efe0ac", + "baec13ec-c42b-51b4-9974-8ef1c2d10ddc", + "5a2221e0-dabc-523c-8358-3e43789e8f7a", + "e99fe157-eda9-5e56-9ec9-8f428de2a161" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "f16c4c6e-bb5f-5d4a-9945-8af4d0df19f4", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "75b4ae7d-7abf-57b8-bda9-5b022d698ae6", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "0f49b102-1d7e-5702-af30-35e5f2ed93a6", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "0be842b8-7f69-503b-baed-c336e5c834d6" + ], + "id": [ + "chatcmpl-AIFqiY2VOktGY4xVSkvpvMDbynoMw", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "03dbb574-1b16-5300-af34-08b82263388e", + "13fa34fd-9bf6-5ae5-8a7e-e1998d56d084", + "527419f1-075d-5d53-a8b5-1685952ecdb0", + "3a807b66-fcae-5cae-b8ad-83a5c6815221", + "b63c48dd-b954-56d4-bdfa-8ab135e7bf47", + "ee3d0900-a422-59cd-a6db-308f20052cc0", + "2aa9f009-ae05-5c93-ac3a-58b1f516d844", + "353dc970-3106-5bbe-8a58-d65d13e5e6ee", + "6c14eef8-bb27-503a-9523-9e7a16d71021" + ], + "contexts": [ + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "Metabolic Syndrome and Family History of Diabetes Public Health Genomics 2010;13:353359 357able difference in the odds between these 2 risk levels. This table indicates that, compared with the average fa-milial risk, a moderate or high familial risk of diabetes increases the odds for each single component of the met-a b o l i c s y n d r o m e . T h e s e o d d s v a r y f r o m 1 . 1 9 ( 9 5 % C I : 0.881.61) to 1.53 (95% CI: 1.301.81). C o n c l u s i o n", + "For type 2 diabetes, there have been a few studies utilising a candidate-gene approach as well as genome-wide association studies, although some argue that genetic factors play only a minor role among Caribbean populations [ 90 ]. A family history of diabetes in any rst- degree relative (parent, sibling) or in a grandpar-ent is associated with a two- to fourfold increased risk of diabetes [ 10 , 91 ]. A family history of dia-", + "evidenced by a very high positive rate of family history of diabetes, and drastically different prevalence in various ethnic groups. Therefore, there is no doubt that type 2 diabetes is a disease with a strong genetic influence. However, the prediction of the relative contribution of genetic influence and number of genes involved in the pathogenesis of the disease has changed in the past few years. Initially, enthusiastic searches of diabetes genes were", + "can decrease risk of diabetes.22 Diet may also play a role. High calorie diets, including those high in fat, and especially saturated fat, have been implicated in the development of type 2 diabetes?4-26 Family history is a very strong risk factor for type 2 diabetes. A strong genetic component is suggested by the 58-75% concordance rates for type 2 diabetes observed in identical twins (Table 3).3 Table 3. Estimated risk of developing type 2 diabetes by family history One parent with type 2 diabetes", + "The fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk. The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this disease if one twin developed type 2 diabetes, then the other one invariably developed it (9). However, this kind of study", + "genetic factors play an important role in the susceptibility to T2D. The risk of the disease developing at some point of life is ~70% when both parents are diabetic and ~40% when one parent has T2D [ 4]. Furthermore, latest data show that more than 400 genetic risk variants at 250 loci for T2D have been Genes 2018 ,9, 374; doi:10.3390/genes9080374 www.mdpi.com/journal/genes", + "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic and clinical relevance. Eur J Clin Invest 2011; 41: 67992. 37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811. 38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identi ed through large-scale association analysis. Nat Genet 2010; 42: 57989.", + "long follow-up. Type 2 diabetes and impaired glucose tolerance (IGT) cluster in families. Thus, most patients have a positive family history, and the lifetime risk for developing type 2 diabetes is increased up to 40% (more than five times the background rate) by having a first degree relative with the disease. If both parents have type 2 diabetes the risk to the offspring may be as high as 70%. Available evidence supports a polygenic mode of inheritance with a considerable environmental input. 1", + "Genetic factors Type 2 diabetes has a strong genetic component and most Asian patients have a rst-degree relative with diabetes. 48,49 Much progress has been made in our understanding of the genetics of this disease. Importantly, most of the loci originally associated with diabetes in European populations have been replicated in Asian populations. Whereas monogenic forms of diabetes result from rare genetic mutations with large e ects, such as those seen in maturity-onset diabetes of young people," + ], + "answer": "Family genetics play a significant role in the likelihood of developing diabetes. Several studies and observations highlight this connection: 1. Genetic factors are important in determining an individual's risk of developing diabetes [1]. 2. A family history of diabetes, particularly in first-degree relatives such as parents or siblings, is associated with a two- to fourfold increased risk of developing diabetes [3]. 3. Type 2 diabetes is strongly influenced by genetics, as evidenced by high concordance rates in identical twins, with studies showing a 58-75% concordance rate [5]. In some studies, the concordance rate is reported to be nearly 100% [6]. 4. The risk of developing type 2 diabetes is approximately 70% if both parents have the disease and about 40% if one parent is affected [7], [9]. 5. Type 2 diabetes clusters in families, and having a first-degree relative with the disease increases the lifetime risk significantly, up to 40% or more [9]. Overall, these findings underscore the strong genetic component in the susceptibility to diabetes, particularly type 2 diabetes.", + "question": "What role do family genetics play in the likelihood of getting diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_14 new file mode 100644 index 0000000..e573f24 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2013 - Gene-Environment and Gene-Treatment.pdf", + "2011 - Interaction Between Exercise and Genetics.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf" + ], + "extraction_id": [ + "751ccb98-2846-5ca7-8ab8-2684100c28fa", + "0504a937-6b88-5004-a13e-5e9c3073eaf6", + "0504a937-6b88-5004-a13e-5e9c3073eaf6", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "93638ea5-6d1f-5b6a-9629-798804de24dd", + "6283c124-b479-5050-86ca-dc42390147a1", + "ee6a4bf3-6f68-58e7-a96f-c879b5269694", + "ed6dcfee-8273-5512-8fb4-fc51a9c921da", + "89bf4316-d0cc-5310-a45e-1dd8b8aefe1b", + "3bf3c6a7-de03-5114-bad8-d53fd76d0fba" + ], + "document_id": [ + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "fe958fb1-5408-56ec-b102-ccf07b4bac2d", + "c36db75e-4b76-540d-9efb-d0e156e61541", + "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "17637a6f-804e-50e4-9cf5-37318e17f15c" + ], + "id": [ + "chatcmpl-AIFqrzKmzcOBxhh6XTfMBqYsubXv7", + "a1c71566-1d75-551a-8588-9a05436545dc", + "fe89ba68-d709-5494-bcdc-82d81e1498d1", + "799f3578-a7ac-551f-b84a-b9fb3be53040", + "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "be87703d-e7b2-5db5-9983-5412e09a57ba", + "89339b65-325f-588f-9f25-761124f0012f", + "fe35615a-6df7-548c-b313-4abca69b1e2d", + "68a382e9-85e0-548c-910e-5f24cb48f9c8", + "6b83f0af-1145-5679-9dae-0f645771d25d", + "1b364e28-08e2-5813-b066-7ce37eeb36cf" + ], + "contexts": [ + "of a given genetic variant is modified by the environ - mental milieu (and vice versa). Evidence that lifestyle factors modify the genetic effects on T2DM risk has been generated from both observational studies and clinical trials82. However, genetic background might also affect the individuals response to lifestyle interventions83. In addition, replication data are sparse, and comprehensive, large-scale studies have failed to provide a compelling", + "genetic risk for diabetes may not moti-vate improvements in lifestyle behaviors.Indeed, knowledge of increased geneticrisk for diabetes may decrease motiva-tion to modify behavior in genetic fatal-ists (83). Diet recommendations optimized to the individual have been shown to re-duce postprandial glycemic excursionsto a greater extent than standard approaches in healthy individuals (84).Meal compositions that induce the most favorable glycemic pro les have been", + "diabetes regardless of the underlying genetic risk. This contrasts with theextensive epidemiological evidence sug-gesting that the relationship of lifestylewith obesity is dependent on genetic risk(7881); however, with few exceptions (e.g., [74]), analyses in large randomizedcontrolled trials have failed to show thatthese same genetic variants modifyweight loss in response to lifestyle in-tervention (82). It is also important to recognize that knowledge of increased", + "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "suggested to attenuate its negative e ect on metabolic pro le, body weight, and diabetes risk ( Franks et al., 2007 ; Kilpelainen et al., 2008 ; Lindi et al., 2002 ; Ruchat et al., 2010 ) ( Table 1 ). The notion that lifestyle modi cation can eliminate the increased risk for development of T2DM in subjects with genetic suscepti-bility is also supported by ndings of Barwell et al. (2008) who", + "proven particularly effective for preven-tion and management of type 2 diabetes.For example, improvement in dietaryquality, in conjunction with other lifestylemodications like increased physical ac-tivity, was shown to be more effectivethan pharmacological treatment in pre-vention of diabetes in individuals at highrisk (1). Further, lifestyle modicationmay mitigate the risk associated with thestrongest known diabetes risk loci (2).While the existence of environmental in-uences on genetic risk (and vice", + "who is lean, genetic risk factors are more likely to be present than in someone who is obese and develops the disease or that weight loss enhances the genetic risk ofdiabetes. Genetic analyses performed in clinical trials involving intensive lifestyle modi - cation provide an important adjunct to the epidemiological literature on gene- lifestyle interactions in type 2 diabetes.On one hand, a major advantage of ran- domized controlled trials is that interac-", + "Lifestyle behaviors and genetic loci have clear and distinguishable effects on T2D risk; however, the pattern of disease occurrence within and between popula-tions that differ in their genetic and environmental underpinnings suggests T2D is caused in part by the interaction between adverse lifestyle behaviors and the genetic profile of an individual. For many, this seems a reasonable assumption, but there is little robust empirical evidence supporting the presence of such interactions.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate", + "Susceptibility to obesity and diabetes is deter- mined by both genetic and lifestyle factors.Suggestive evidence of genelifestyle interac- tion (Box 33.3) in the development of common diseases such as obesity and type 2 diabetes wasrst provided by descriptive epidemiological studies such as migration studies that compare the disease risk between genetically related pop-ulations who live different lifestyles. A classicalexample is the comparison of the risk of obesity" + ], + "answer": "Yes, lifestyle changes can affect genetic risk factors for diabetes. Evidence from both observational studies and clinical trials suggests that lifestyle factors can modify the genetic effects on the risk of developing type 2 diabetes mellitus (T2DM) [1]. Lifestyle modifications, such as improvements in dietary quality and increased physical activity, have been shown to be particularly effective in preventing and managing type 2 diabetes, even in individuals with a high genetic risk [6]. Furthermore, behavioral changes can substantially mitigate the diabetogenic effects of individual or multiple risk alleles, indicating that the adverse effects of some established T2D-associated loci may be greatly attenuated by appropriate lifestyle changes [9].", + "question": "Can lifestyle changes affect genetic risk factors for diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_15 new file mode 100644 index 0000000..1214d93 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Genetic studies of diabetes following the advent of the genome-wide association study where do we go from here.pdf", + "2011 - Genome-wide association studies (GWAS) impact.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2007 - Genetic studies of diabetes following the advent of the genome-wide association study where do we go from here.pdf", + "2017 - Differentiation of Diabetes by Pathophysiology.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf" + ], + "extraction_id": [ + "1a155200-3610-528f-a51d-b2f27562037a", + "cf06774a-9e13-59fd-9652-d5013ef83387", + "238129d2-439f-5a25-8e86-297e7a69d81c", + "6b04dc27-e7ff-53c8-9021-a3cdb5415059", + "1a155200-3610-528f-a51d-b2f27562037a", + "a9accd40-eb89-5595-bf27-b6b82b49f4d4", + "40190f1d-aad5-5d71-b5ba-78331d5e3abb", + "cd034e2b-72bd-5cda-a456-48cf17ead1bf", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "9190d1c1-41a4-5af3-a570-7fea6a15e71a" + ], + "document_id": [ + "7b96d9b2-6494-5c20-9693-dc146a4e347c", + "086c6869-7c70-5364-9269-760267fb458d", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "7b96d9b2-6494-5c20-9693-dc146a4e347c", + "9cfaef1e-fb60-5c2b-94f0-632c89b2eb16", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "ab2868dd-62f6-5350-994c-fcea4328e8a3" + ], + "id": [ + "chatcmpl-AIFqw6zPQKQT7tNlJNiyf2dx560ep", + "9250b1a6-26d8-5c38-840f-547a9647e809", + "9d55a0b9-d125-587d-b21e-f4bd55b8de28", + "0372a2d5-28c0-5369-8f05-18f7124eb4ae", + "5b134bfd-6af3-5189-b144-57bf70c2cf20", + "3cd5df03-7c2b-585c-a3bb-67dc0e1c615c", + "9b04e578-bfe5-5f3c-8556-aac26d6429cc", + "f3c6864c-7c06-5a61-bdda-d5730821c237", + "81e7ee8d-adb5-5fd7-a3b1-1f6bfb059974", + "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "23321ca3-f73d-5542-a6c0-1133c3d3e9e5" + ], + "contexts": [ + "understanding of the genetic basis of diabetes, and the advances of recent months are arguably the most important made since the role of the HLA region was recognised in type1 diabetes. The number of genetic regions causally implicated is now 11 each for type 1 and type 2 diabetes [ 19], and is set to rise further. The bewildering pace of new discovery standsin stark contrast to the slow progress that characterised the previous two decades, with a total combined output of three", + "It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes. Unlike previous approaches, genome-wide association studies have extensively delivered on the promise of uncovering genetic determinants of complexdiseases, with a number of novel disease-associated variants being largelyreplicated by independent groups. This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and", + "The history of diabetes genetics traces human genetic research more broadly.Initially, only a few polymorphic genetic markers were known, and these werestudiedinpopulation-basedassociationstudies.Withthedevelopmentofgenome-wide maps for family-based linkage analysis and of positional cloning, attentionturned to monogenic forms of disease. The application of family-based linkagemethods to common forms of diabetes, however, met with less clear success.More recently, with progress in genome sequencing and", + "the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development[142-144]. However , despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive[145,146]. Evidently, a major complication is the", + "confirmed genes for type 2 diabetes and six for type 1(Fig. 1). At last, it seems, our understanding of the genetic basis of complex, multifactorial forms of diabetes is catching up with that of rarer, single-gene disorders. This leap in knowledge is the result of major advances in technology plus an improved understanding of patterns of human genetic variation. Using single nucleotide polymor- phism (SNP) chips it is now possible to analyse up to a million", + "make dissection of the black box of genetics of diabetespossible in the near future, but at this point, apart fromthe pro les that distinguish between type 1 and type 2 diabetes and a limited number of speci c variants that identify small subgroups of patients (MODY), genetics has not been successful in further differentiating subclasses ofdiabetes. Research Gaps After consideration of the known genetic associations with diabetes risk, consensus developed that the eld is", + "studies provide new insights into type 2diabetes aetiology. Nat Rev Genet 2007;8:657662 11. Grant RW, Moore AF, Florez JC. Genetic architecture of type 2 diabetes: recentprogress and clinical implications. Diabe-tes Care 2009;32:11071114 12. Dupuis J, Langenberg C, Prokopenko I,", + "early results have been excellent, yielding six new replicating gene regions. Here I discuss the insights into type 2 diabetes genetics that have been provided by these new findings. I consider where diabe - tes genetic studies might go from here, and present a perspective that may be applicable to other common traits. I also briefly discuss the wider implications that surround the identification of a common gene that predis - poses to type", + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "24. Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl. Acad. Sci. USA 114, 23012306 (2017). 25. Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 diabetes susceptibility loci. eLife 7, e31977 (2018). 26. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 14151425 (2015)." + ], + "answer": "Recent breakthroughs in understanding the genetic causes of diabetes include several key advancements: 1. The number of genetic regions causally implicated in both type 1 and type 2 diabetes has increased to 11 each, marking significant progress in identifying genetic factors associated with these conditions [1]. 2. Genome-wide association studies have been particularly successful in uncovering genetic determinants of complex diseases like diabetes. These studies have identified a number of novel disease-associated variants, which have been largely replicated by independent research groups, highlighting their reliability and significance [2]. 3. Advances in technology, such as the use of single nucleotide polymorphism (SNP) chips, have enabled the analysis of up to a million genetic variations. This technological leap has greatly enhanced our understanding of the genetic basis of complex, multifactorial forms of diabetes, bringing it closer to the understanding of rarer, single-gene disorders [5]. 4. Recent studies have yielded six new replicating gene regions associated with type 2 diabetes, providing new insights into the genetic architecture of the disease and suggesting potential directions for future research [8]. These breakthroughs represent a significant leap forward in the genetic understanding of diabetes, driven by technological advancements and collaborative research efforts.", + "question": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_16 new file mode 100644 index 0000000..8f5ae28 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "2015 - Gestational Diabetes Alters Offspring DNA.pdf", + "2010 - Diabetes in Asia.pdf", + "2016 - Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes.pdf", + "2021 - Interpreting type 1 diabetes risk.pdf", + "2010 - Common Inherited Variation in Mitochondrial Genes.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf" + ], + "extraction_id": [ + "9369222f-e125-58c0-8f2b-cf5daa867f77", + "c9f74729-056d-556f-8aa8-e0f7a7bd6b66", + "c9f74729-056d-556f-8aa8-e0f7a7bd6b66", + "a9ec4c4f-b038-52d2-90db-7bee1ef1f78c", + "af8de1bb-e71e-514f-a5eb-59f37498028e", + "510b7c7b-ccbb-5d0d-b654-e1dbcf859cb7", + "af25ae45-0c5f-5492-86d0-734eb0fbac12", + "0bd0fae3-9297-5da2-b3ae-21190af84094", + "930831e1-56c3-5ef6-b847-2f25f2567032", + "8248ba2b-335c-53ce-afbe-f31b68507443" + ], + "document_id": [ + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "98564dd2-424b-557a-a539-022508283567", + "98564dd2-424b-557a-a539-022508283567", + "98564dd2-424b-557a-a539-022508283567", + "59b51d61-b2c4-540b-a2fb-4c56badb26c1", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "5b239c51-7b4c-58e0-acca-2061593fe317", + "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "9a5c8cba-06cb-5280-871f-1bbe128c3dc4", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d" + ], + "id": [ + "chatcmpl-AIFr5iGpAfX0NjS78CDO8cOuBpwUj", + "3bbf736e-7d8b-5e67-a4bf-e1ae28738bf3", + "ccf2d9af-4dca-5021-9c9d-301f817f80e4", + "d580609b-d24b-5718-ab63-0e6088c8bfeb", + "3f90af62-9a1d-5ac2-b5ee-a616857b34df", + "c171a147-2cf6-5340-82d4-caa63cdafbbd", + "81eb21fb-488a-5b08-b883-cd8780110c66", + "9b60d258-714a-5e70-b2fa-b0a29fc0d672", + "dd3348a8-1f07-5e6d-8ba0-3c6c263c0799", + "9080e28b-1c0d-5bfa-8698-7ae677aa64ed", + "c2f1a416-7f04-55b0-b19b-8a8aa858b801" + ], + "contexts": [ + "genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. Thisseems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with geneticallyprogrammed bcell dysfunction to precipitate diabetes. Citation: Jain P, Vig S, Datta M, Jindel D, Mathur AK, et al. (2013) Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS ONE 8(1): e53522. doi:10.1371/journal.pone.0053522", + "have been the subject of most follow-up studies to date.Specifically, we examined acute changes in expression of these genes in response to feeding and fasting and longer term changes in the expression of these genes inresponse to a diet high in fat and sugar, recognized as a critical environmental risk factor for type 2 diabetes. It has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6]. Consistent with this,", + "or survival. However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues. Genes affecting insulinsensitivity may be expected to be expressed in peripheralinsulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status. Consumption of a high fat diet was associated with a tendency for the ex- pression of several of these genes to be decreased. Simi-larly, many of the genes were regulated by feeding and", + "secretion versus insulin sensitivity). We also sought todetermine whether any of these genes are regulated by conditions known to alter the expression of metabolic- ally relevant genes. We examined the expression of thesegenes under fasting and non-fasting conditions (e.g. in response to insulin), which might be altered if they affect peripheral insulin sensitivity. Consumption of diets high in fats and sugars is associated with risk of developing type 2 diabetes [34] and many genes that are critical for", + "regulating sugar metabolism. Moreover, genes that were", + "Figure 2: The role of type 2 diabetes genes in insulin secretion Pancreatic -cell genes associated with type 2 diabetes are in italics. G6P=glucose-6-phosphate. Adapted from Florez JC. Newly identi ed loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia 2008; 51: 110010, by kind permission of the author and Springer Science + Business Media. Positive calorie balance Cycle A++ Cycle B Liver fat Insulin suppression of", + "tive Glis3 expression, which in turn drive increased levels of beta cell apoptosis and senescence. Genetic susceptibility could be replicated by elevated levels of dietary fat. Transcriptional analysis of human islets identified the same genetic networks at play. Together, these findings demonstrate both the important role of genetic variation in beta cells for diabetes susceptibility and a mechanism by which the Western diet may contribute to the growing diabetes epidemic. RESULTS", + "associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 26242634 (2011). 65. Saxena, R. etal. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142148 (2010). 66. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441447 (2010).", + "38. Saxena R, Hivert M, Langenberg C, Tanaka T, Pankow JS, et al. (2010) Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 42: 142148. doi:10.1038/ng.521. 39. Neale BM, Sham PC (2004) The future of association studies: gene-based analysis and replication. Am J Hum Genet 75: 353362. doi:10.1086/423901. 40. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, et al. (2007)", + "Nature Reviews | EndocrinologyFactors that aect insulin secretion and action Body weight Level of physical activity Smoking Heavy alcohol consumption Genetic predisposition Geneenvironment interaction Positive risk prole Negative risk prole Normoglycaemia/uni03B2-cell dysfunction and insulin resistanceAdipose tissue Skeletal muscle LiverInsulin-mediated glucose production /uni2191Insulin-mediated glucose uptake /uni2193 Insulin-mediated glucose uptake /uni2193 Hyperglycaemia Epigenetics" + ], + "answer": "Genes influence how our bodies respond to sugar and insulin in several ways: 1. **Insulin Secretion and Resistance**: Genes are directly related to insulin secretion and indirectly influence insulin resistance by interacting with other genes. This interaction supports the idea that environmentally triggered insulin resistance can interact with genetically programmed beta-cell dysfunction, leading to diabetes [1]. 2. **Beta-Cell Function**: Many genetic variants primarily affect beta-cell function, development, or survival, rather than insulin sensitivity. This suggests that genetic factors play a significant role in how beta cells respond to sugar and insulin [2]. 3. **Expression in Metabolically Relevant Tissues**: Genes affecting insulin sensitivity are often expressed in peripheral insulin-sensitive tissues, such as the liver and adipose tissue, and their expression can be responsive to metabolic status. For instance, a high-fat diet can decrease the expression of several of these genes, indicating a genetic influence on how the body responds to dietary changes [3]. 4. **Regulation by Metabolic Conditions**: The expression of certain genes can be altered by conditions such as fasting and feeding, which are known to affect peripheral insulin sensitivity. This suggests that genetic regulation can influence how the body responds to changes in sugar and insulin levels [4]. 5. **Genetic Variation and Insulin Response**: Specific genetic variations, such as those in the GIPR gene, can influence glucose and insulin responses to an oral glucose challenge, highlighting the role of genetic differences in individual responses to sugar intake [9]. Overall, genetic factors can influence both the secretion of insulin and the body's sensitivity to it, affecting how we metabolize sugar and respond to dietary changes.", + "question": "How do genes influence how our bodies respond to sugar and insulin?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_17 new file mode 100644 index 0000000..4824400 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 -precision-medicine-for-managing-diabetes.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2021 - Interpreting type 1 diabetes risk.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "2011 - Shared Genomics of Type 2 and Gestational Diabetes Mellitus.pdf", + "2019 - Biomarkers for type 2 diabetes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf" + ], + "extraction_id": [ + "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "2610c9c1-5e75-528e-98d8-c4a543ea2f89", + "254be2dd-1b4f-5cf9-af93-dbf3d5867510", + "640f3749-a2bf-5b6b-adab-72ce7f029a28", + "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "41fefdf5-447e-556e-b95f-c132bdea7c41", + "bc4717c3-d353-5f44-9513-50634f8d5196", + "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07", + "0aae948a-50f9-568a-b0dc-5960a2d2ceaa", + "38bacfcd-d182-5220-b8bc-18f6c74b14a8" + ], + "document_id": [ + "80949bab-d085-5f61-b98a-4bee043bc4e2", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "bef0cabe-0bca-5715-9ffc-0b825744fbcf", + "c8ee94fc-f9bc-5a32-9524-9d1d9cf37159", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d" + ], + "id": [ + "chatcmpl-AIFrBAew5HsqHnMUVkuc9dpSmo0io", + "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "988cae28-e149-5190-8ff0-6ecce8d001bc", + "ca9e53b7-6e51-5ae6-9ef4-8f2f5f40acb5", + "61f523a8-f466-5148-afba-6400c44ed278", + "151d8a78-8aa8-5024-8e15-54fba4f1857b", + "f692be48-b905-5463-8101-22eaf14e6405", + "48c93a37-d0d5-51de-b2d1-5c6122c01ab1", + "82debd98-f2fe-51aa-931c-63e11249de7b", + "8469faae-c6c9-5fd4-8437-870eef394dd1", + "387e1774-0250-5c72-b11c-069bdf3ef9ea" + ], + "contexts": [ + "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12 Also, several", + "Genetics of Type 2 Diabetes Chapter 12 197400 multiallelic markers (short tandem repeats or microsatellites, with a density of 1 marker/10 cmol) allows identi cation of polymorphic markers showing strong allele identity by descent in diabetic family members (i.e. allele sharing in sibships is signi - cantly higher than 50%). Once identi ed, such susceptibility genes for diabetes may then be positionally cloned in the intervals of linkage.", + "3. Katsarou, A. etal. Type 1 diabetes mellitus. Nat. Rev. Dis. Primers 3, 17016 (2017). 4. Onengut-Gumuscu, S. etal. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381386 (2015). 5. Barrett, J. C. etal. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703707 (2009).", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2229(Fig. 3). An increase in the BMI and a concomi - tant decrease in insulin sensitivity during the 8-year period were consistent findings, with no differences between subjects at high and low genetic risk (Fig. 3A and 3B). However, subjects with a high genetic risk did not increase their insulin secretion (disposition index) to compen -", + "and genetic markers to improve the prediction of type 2 diabetes: theEPIC-Potsdam Study. Diabetes Care . 2009;32:2116 2119 (in eng). 56. Cauchi S, Meyre D, Durand E, et al. Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. PLoS One . 2008;3(5): e2031 . 57. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med . 2008;359:2220 2232 (in eng).", + "etically expressed homeobox variant (rs1111875) on type 2 diabetes risk. Molecular Genetics and Metabolism , 102 (2), 194199. Watanabe, R. M., Black, M. H., Xiang, A. H., Allayee, H., Lawrence, J. M., & Buchanan, T. A. (2007). Genetics of gestational diabetes mellitus and type 2 diabetes. Diabetes Care , 30 (Suppl. 2), S134S140. Williams, M. A., Qiu, C., Dempsey , J. C., & Luthy , D. A. (2003). Familial aggregation of type 2", + "markers, genetic markers do not change with disease progression.Dimas and collaborators examined the association of 37 establishedT2D susceptibility loci and indices of proinsulin processing, insulin secretion, and insulin sensitivity in 58,614 nondiabetic subjects [6]. Cluster analysis classi ed the risk loci into ve major categories on the basis of their association with glycemic phenotypes. The rst cluster was characterized by the effects of the risk alleles of PPARG ,KLF14 ,", + "recently, meta-analysis of GWAS data involving African American type 2 diabetes patients identified similar loci to the previous studies with the addition of two novel loci, HLA-B and INS-IGF[157]. These results provide strong evidence of common genetic determinants including common specific genes that are linked to diabetes. A small list of specific genetic markers seem strongly associated with the risk of developing type 2 diabetes including the TCF7L2[158] and CAPN10[159,160]", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in the Botnia subjects (P = 0.006). Of the 16 loci that have been associated with type 2 diabetes previously,8-15 we showed that 11 TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX were associated with an enhanced risk of future", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2227(Fig. 1B), whereas impaired fasting glucose or impaired glucose tolerance developed in 313 of 2039 subjects (15.4%). Clinical Factors Predicting Incidence of Diabetes In both the MPP and Botnia studies, a family his - tory of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of tri -" + ], + "answer": "Yes, there are specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes. For Type 1 diabetes, genome-wide association studies have identified over 50 loci associated with an increased genetic risk. Several candidate genes within these regions have been suggested or identified, although the exact molecular mechanisms by which they contribute to islet cell inflammation and beta cell destruction are not fully understood [1]. For Type 2 diabetes, specific genetic markers have also been identified. Some of the loci associated with an increased risk include TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX [9]. Additionally, markers such as TCF7L2 and CAPN10 have been strongly associated with the risk of developing Type 2 diabetes [8]. These findings indicate that while both types of diabetes have genetic components, the specific markers and loci associated with each type differ, reflecting their distinct pathophysiological mechanisms.", + "question": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_18 new file mode 100644 index 0000000..fbd2614 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2018 - A theoretical framework for interaction of nursing discipline.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2005 - Animal models of diabetes mellitus.pdf", + "2017 - Painting a new picture of personalised medicine for diabetes.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf" + ], + "extraction_id": [ + "a5ae065c-371f-5459-830b-7a34891ca091", + "6b2ac076-ee4b-53b3-b49b-1d15f46e6a98", + "c4de4c07-4749-5401-bbf3-16988c132852", + "48643e77-c5b4-5042-8f08-82c986d9f5b2", + "abf78c3a-ad53-5c86-979d-2d9d176a51a4", + "168e94e9-e8c2-547c-878a-1e5306564193", + "3dca156c-64c4-577f-b0a6-069de0f31234", + "1cd3076d-af86-55d7-903c-9065bc640af0", + "6b2ac076-ee4b-53b3-b49b-1d15f46e6a98", + "168e94e9-e8c2-547c-878a-1e5306564193" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "4c90f95f-3365-522e-9eb4-9ea002beddb2", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "e226b2b1-0bc4-5d79-b931-ad47f21be045", + "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "ec4921c2-af14-56cc-aed3-65f8ea236bde" + ], + "id": [ + "chatcmpl-AIFrIc4qRPbtDcHbcrNOicZwU9hKr", + "59cce584-cd38-52d1-bdaa-d5500175eefb", + "d37e62ab-6261-5f14-8423-3b6e2574422e", + "f4e8a3c8-0b85-5595-8917-933aced8b3ba", + "cb7178a0-7015-555c-801a-cd2d258cf3dc", + "d5963c8e-686f-52f5-a6de-b978d5c40e20", + "0b4a495d-fdee-515a-a524-d9415b17f97e", + "13b73999-262c-50e1-b668-2d5f7ca02067", + "1299cc23-f6b0-5801-bead-b46ac90bc3a8", + "3201da93-5a34-5164-8bf4-c98d32019019", + "0418b345-7005-5d7d-a79f-570fb61bd14b" + ], + "contexts": [ + "unraveling the pathophysiological mechanisms of this disease, identifying candidate diabetic genes, and discovering and testing new therapeutic agents. The classical rodent models of diabetes allow unbiased discovery, while the new models made by genetic manipulation allow testing of the role of specific genes and tissues. Experimental animal models are an irreplaceable resource for diabetes research and are hastening the progress towards the goals of better treatment, prevention, and cure.", + "is absence of reliable methods for generating specific celltypes,immunologicalrejectionofthetransplantedcells,anddifficulty in purification of specific lineages [55]. Furtherconcernsincludetheuncontrolledproliferationofthetrans-planted embryonic stem cells into a specific type, once theyaretransplanted[56].Still,despiteofitsmanifoldlimitationsboth scientific and ethical, the application of stem cell tech-nologyholdsimmenseprospectsintreatmentofdiabetes. 6. Gene Therapy in Diabetes", + "T ogether, these discoveries will continue to improve our understanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to chronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene - based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "Greatstrideshavebeenmadeclinicallyintheprevention, development,andtreatmentofthediseasebutnotherapeuticmethod have been completely successful till date. With newtechnologies revolutionizing the treatment possibilities, thesearch for an effective medication is not far ahead. Theextensive research leading to the discovery of the pathwaygenes contributing to the development of the disease andthe sequencing of complete genomes have revolutionized the diabetes research. The development of the techniques", + "into different genetic levels of disease categories, from which pre- vention or treatment methods could be provided accordingly [ 4]. For example, some forms of diabetes are directly related to a change in a single gene [ 34]. Some patients who are diagnosed with type 1 diabetes can now be tested for one of monogenic diabetes. The appropriate treatment for these patients is not injecting insulin, but giving oral sulfonylureas [ 34]. Moreover, it is now well understood", + "pp .430435,2003. [58] M. Zalzman, S. Gupta, R. K. Giri et al., Reversal of hyperglycemia in mice by using human expandable insulin- producing cells differentiated from fetal liver progenitor cells,Proceedings of the National Academy of Sciences of the United StatesofAmerica ,vol.100,no .12,pp .72537258,2003. [59] H.-S. Jun and J.-W. Yoon, Approaches for the cure of type 1 diabetes by cellular and gene therapy, Current Gene Therapy , vol.5,no.2,pp.249262,2005.", + "transgenics. It is likely that animal models will play an importantrole in the eventual cure of human diabetes mellitus. Competing interests None declared. References 1Sima AAF, Shafrir E, eds. Animal Models of Diabetes: A Primer. Amsterdam: Harwood Academic Publishers, 2000. 2British Union for the Abolition of Vivisection. Home page. Available from: http://www.buav.org. 3Patterson C. Eternal Treblinka. Our Treatment of Animals and the Holocaust . New York: Lantern Books, 2002. 4Regan T.", + "Third, this view of diabetes pathogenesis is consistent with the growing portfolio of available therapies. We have agents and interventions that can prevent or ameliorate diabetesthrough, for example, beneficial effects on islet function (e.g. sulfonylureas), obesity (weight loss), insulin resistance (e.g. exercise), fuel partitioning (e.g. thiazolidinediones) andmicrobiome content (metformin, possibly). Just as diabetes risk alleles influence metabolic phenotype through pushing", + "aprospectivetherapeuticapproachfortype1diabetes[59]. Thein vivogene therapy is the method of choice as a therapeutic strategy because it is simpler and the vectorcontaining the desired gene is directly inserted into thepatient, but the development of safe (not toxic to host)and effective vectors remains as a challenging task for genetherapist. Presently, the strategies for in vivotherapy involve", + "betacellulin gene therapy induces islet neogenesis in the liver a n dr e v e r s e sd i a b e t e si nm i c e , Nature Medicine ,v o l .9 ,n o .5 , pp.596603,2003. [73] S. Ferber, A. Halkin, H. Cohen et al., Pancreatic and duode- nal homeobox gene 1 induces expression of insulin genes inliver and ameliorates streptozotocin-induced hyperglycemia, Nature Medicine ,vol.6,no .5,pp .568572,2000. [74] P.A.Halban,S.E.Kahn, A.Lernmark,andC.J.Rhodes,Gene andcell-replacementtherapyinthetreatmentoftype1diabetes." + ], + "answer": "New gene therapies have the potential to cure or treat diabetes through several innovative approaches: 1. **Gene-Based Therapies**: Advances in understanding the biological mechanisms that maintain glucose homeostasis and the molecular defects leading to chronic hyperglycemia could lead to the development of gene-based therapies. These therapies aim to target specific genetic factors involved in diabetes, potentially offering more precise treatment options [3]. 2. **In Vivo Gene Therapy**: This approach involves directly inserting a vector containing the desired gene into the patient. It is considered a promising therapeutic strategy for type 1 diabetes, although challenges remain in developing safe and effective vectors [9]. 3. **Inducing Islet Neogenesis**: Gene therapy techniques, such as betacellulin gene therapy, have been shown to induce islet neogenesis in the liver and reverse diabetes in mice. This suggests that gene therapy can stimulate the body to produce insulin-producing cells, offering a potential cure for diabetes [10]. These strategies highlight the potential of gene therapies to address the underlying genetic causes of diabetes and restore normal insulin production and glucose regulation.", + "question": "How can new gene therapies potentially cure or treat diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_19 new file mode 100644 index 0000000..f60ea60 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf", + "2015 - Diabetes mellitus The epidemic of the century.pdf", + "2007 - Pharmacogenetics of metformin response a step in the path toward personalized medicine.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2012 - Systems Biology Approaches to Nutrition.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2004 - Diabetes Genes a.pdf", + "2019 - HDAC4 mutations cause diabetes and induce \u2010cell FoxO1 nuclear exclusion.pdf", + "2005 - Type 2 diabetes mellitus from genes to disease.pdf", + "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf" + ], + "extraction_id": [ + "29df597d-e40d-5bc8-8ee0-89141d8e7fc0", + "e119acfb-4ad6-515e-a1bb-7796d283befc", + "c66bcb9f-15af-5843-9e9c-168e8cf230d0", + "38df3fac-cb86-5e74-b270-1e1e9e12dcdb", + "8d7fefe4-325f-5c64-9fee-0587c545d5ab", + "5a39ee4b-ba00-56d6-ba6c-0edeac3b4f2e", + "c6bf083c-f045-55e2-9eae-ff96a4ceea4c", + "7f53ea65-79ed-5207-9397-68b6d14bc19c", + "d79047d9-58d0-5440-b63c-e648b5df5538", + "29df597d-e40d-5bc8-8ee0-89141d8e7fc0" + ], + "document_id": [ + "e315a891-ba59-57e9-856b-602544375324", + "e114dd28-fd39-56df-bdeb-8806474a6c10", + "12344230-0ed1-516f-bf2d-9c6e71ac76b5", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "7e4028b2-d5c1-5ddc-a06d-fd4f767d0f39", + "52687a38-6a4b-51d2-aafa-812c76981dfe", + "e315a891-ba59-57e9-856b-602544375324" + ], + "id": [ + "chatcmpl-AIFrQL8Phj0Q41oiXeOOZsabJcULg", + "c38627c6-0dd1-5dec-aeb6-ea1edb924480", + "d704148a-88c0-58fe-810a-89b767a1f53b", + "7bbf950d-cbf8-5221-8ea6-b3571fab4fad", + "35efeaf0-c6b6-509e-9426-d23c8727164f", + "55e16624-4a02-5fba-bbe7-a07db8559401", + "854afd34-91ed-5817-b24e-1fd5894261f3", + "a74728c6-2903-5035-afd9-0f6a0f0c295e", + "5b06a49e-7ef9-558a-b402-11866c555cd5", + "fedbf66e-cfd2-52bb-b9db-393d815aade7", + "c48920f3-1236-5921-b2a1-f09edba1e7ec" + ], + "contexts": [ + "to improve diagnosis. Monogenic vs. polygenic diabetes Monogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly penetrant variant in one gene in a given individual, and polygenic diabetes resulting from the contribution of several variants with smaller effects in the context of environmental/lifestyle factors. In T1D, autoimmune dysfunction is the prominent mechanism, with variation in the major histocompatibility", + "represent about 2%-5% of diabetes patients. Mono - genic diabetes results primarily from gene defects that lead to a decrease in beta cell number or function. Monogenic diabetes genes were identified using linkage studies or code for proteins that directly affected glucose homeostasis. The majority of genes responsible for monogenetic diabetes code for either transcription factors that participate in the control of nuclear gene expression or proteins that are located on the cell", + "diabetic patients inwhom rare, highly penetrant mutations ofasingle gene cause their diabetes (13). While com - mon variants ofthese genes that make a small contribution topolygenic diabetes may also exist (13), thevariants causing monogenic diabetes have limited util- ityinpharmacogenetics duetotheir low allele frequency. Thevast majority oftype 2diabetes patients have polygenetic forms ofthedisease that typically also require a permissive environment (e.g., obesity, sed-", + "diabetes exist along more of a continuum than previously appre - ciated. Therefore, knowledge about monogenic diabetes not only provides opportunities for etiology-based treatment of the minori- ty of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology. Types of monogenic diabetes Maturity-onset diabetes of the young MODY comprises most monogenic diabetes cases, with classical characteristics of young diagnosis age, family history of diabe -", + "Monogenic Diabetes Monogenic diabetes is a class of diabetes associated with genetic defects in beta - cell function. They are frequently associated with early onset of hyperglycemia (typically before 25 years of age). Three common forms of mono-genic diabetes include maturity - onset diabetes of the", + "HNF4A-MODY and requires genetic testing to diagnose. Here we will describe monogenic diabetes types, etiologies, diagnosis, management, and strategies to improve diagnosis. Monogenic versus polygenic diabetes Monogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly pene - trant variant in one gene in a given individual and polygenic diabe - tes resulting from the contribution of several variants with smaller", + "Monogenic inheritance is caused by mutation of a single gene. There are some well-defined monogenic rodent models. In humans, monogenic obesity and diabetes exist as well, but are extremely rare. Polygenic inheritance is the result of multiple contributing genes and is the predominant mode of inheritance in human type 2 diabetes. Multiple polygenic animal models are also available. However, even in monogenic animal models, genetic background plays an important influence. For", + "(Mendelian) that may also cause type 2 diabetes (Yang & Chan, 2016). More than twenty genes highly expressed in pancreatic cells have been identified within these mono-genic subtypes (AlkortaAranburu et al., 2014). Recently, two national surveys revealed that most patients with mono-genic diabetes are likely to be unrecognized and misdiag-nosed as type 1 or type 2 diabetes (Delvecchio et al., 2017; Johansson et al., 2017). Genetic diagnosis leads to improved treatment, better prediction of disease", + "Key words: diabetes, gene, polygenic, monogenic Introduction Diabetes is one of the most common metabolic disor - ders. It is estimated that the number of diabetes pa - tients worldwide has already exceeded 200 million [92]. This creates a need to understand the etiology ofthe disease, genetic and enviromental factors influ - encing development of diabetes. Diabetes is a group of metabolic diseases that are characterized by ele - vated glucose level. Poorly controlled or undiagnosed", + "2 1.1.2 Introduction Monogenic diabetes is caused by a single defect in one of over 40 genes1,2. Since MODY (maturity onset diabetes of the young) was named by Fajans for the T2D -like presentation in young people with an autosomal dominant pattern of inheritance3,4, our understanding of phenotypic and genetic heterogeneity in monogenic diabetes has increased. The major monogenic diabetes categories are MODY, neon atal diabetes" + ], + "answer": "Monogenic and polygenic diabetes are distinct forms of diabetes with different genetic underpinnings: 1. **Monogenic Diabetes**: This form of diabetes results from a mutation in a single gene that is highly penetrant, meaning it has a strong effect on the individual who carries it [1], [6]. Monogenic diabetes is often associated with defects in beta-cell function, leading to a decrease in the number or function of these cells [2]. It is typically characterized by early onset, often before the age of 25, and can include conditions like Maturity-Onset Diabetes of the Young (MODY) [5]. Monogenic diabetes is relatively rare, representing about 2%-5% of diabetes cases [2]. 2. **Polygenic Diabetes**: In contrast, polygenic diabetes results from the combined effect of multiple genetic variants, each contributing a small effect, along with environmental and lifestyle factors [1], [6]. This form of diabetes is more common and is the predominant mode of inheritance in type 2 diabetes [7]. The genetic variants involved in polygenic diabetes do not have as strong an effect individually as those in monogenic diabetes, but together they contribute to the disease risk in the presence of other factors like obesity and sedentary lifestyle [3]. In summary, monogenic diabetes is caused by a single gene mutation with a strong effect, while polygenic diabetes involves multiple genes with smaller effects combined with environmental influences.", + "question": "What is the difference between monogenic and polygenic diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_2 new file mode 100644 index 0000000..2222b39 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf", + "2010 - A Genome-Wide Association Study Identifies.pdf", + "2012 - Association between type 2 diabetes genetic susceptibility loci and visceral and subcutaneous fat area as determined by computed tomography.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "2008 - SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes.pdf", + "2010 - Diabetes in Asia.pdf", + "2003 - Genome-wide screen in obese pedigrees with type 2 diabetes.pdf", + "2008 - Clinical Risk Factors, DNA Variants.pdf" + ], + "extraction_id": [ + "e61efd89-f844-5c3a-98b9-1a827b58b507", + "2643b341-8c50-5cea-af36-86a8b070a80e", + "f5b0ecdc-fdf2-5ac3-bebb-9c9ff5863935", + "e0bbfc0e-ae79-568c-b704-96febad87d6f", + "aba850e8-8c0d-5256-b2ba-fa1dfc221114", + "8a28c11f-e0d2-526b-ac85-2f2fbf054fc5", + "706cb4a1-57c4-5b63-9d4e-4a7ea027a8f1", + "11faf4fe-7b71-562e-9901-c428ab20b285", + "1110f7b4-ab5a-5b41-b37d-a992b29cb20c", + "0aae948a-50f9-568a-b0dc-5960a2d2ceaa" + ], + "document_id": [ + "f5096148-3f85-57c1-8414-2f240ea42068", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "0301881d-40dd-5343-b22e-927d58c2cb2a", + "b86d3101-f383-520b-8360-7d80bc7ec6fa", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "3d629777-f1b6-5450-94ef-56736e5a4e10", + "78702b1e-0f14-5757-b967-9bcb7852f6ac", + "0be842b8-7f69-503b-baed-c336e5c834d6", + "335a3c08-14d3-5511-ab84-340e64c6f993", + "0018610a-9c86-5e2d-a27d-f66cf4f8519d" + ], + "id": [ + "chatcmpl-AIFpDYOJMy59ARMmvejZqYaNW81X4", + "47558743-2803-51a7-856b-8f6606712d08", + "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "225792f4-c56b-5139-8bec-d5d1d393a6b2", + "8b718138-167a-50b0-afb7-4b507abc05ff", + "e3cbe02b-9a3c-5b66-a5fb-d9d75b5db3f9", + "f3ce8455-f123-5840-8a50-da7885c7e18d", + "dfba6b2e-1531-5ac4-a41d-aa4a6d76d7e0", + "78d81651-7215-596a-b128-37e429dc7edb", + "b0d3a09d-36a3-5c6e-a110-3fccddaa74b7", + "8469faae-c6c9-5fd4-8437-870eef394dd1" + ], + "contexts": [ + "novel risk loci for type 2 diabetes. Nature 2007, 445(7130) :881-885.5. Gaulton KJ, Willer CJ, Li Y, Scott LJ, Conneely KN, Jackson AU, Duren WL, Chines PS, Narisu N, Bonnycastle LL, et al:Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes. Diabetes 2008, 57(11) :3136-3144. 6. Hu C, Zhang R, Wang C, Wang J, Ma X, Lu J, Qin W, Hou X, Bao Y, Xiang K, et al:PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX,", + "ly associated with type 2 diabetes: TCF7L2, KCNJ11, and PPARG . 5-7 However, in 2007, a number of novel genetic variants ( CDKAL1, IGF2BP2, the locus on chromosome 9 close to CDKN2A/CDKN2B, FTO, HHEX, SLC30A8, and WFS1)8-14 were shown to in - crease susceptibility to type 2 diabetes in repro - ducible studies. Furthermore, a recent meta-analy - sis identified six novel variants ( JAZF1, CDC123/ CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia - betes. 15", + "2009. There are now at least 19 loci containing genes that increase risk of T2D, including PPARG [27], KCNJ11 [27], KCNQ1 [28,29], PLoS Genetics | www.plosgenetics.org 1 February 2010 | Volume 6 | Issue 2 | e1000847", + "et al. Association between type 2 diabetes loci and measures of fatness. PLoS One 5, e8541 (2010). 22 Ng, M. C., Park, K. S., Oh, B., Tam, C. H., Cho, Y. M., Shin, H. D. et al. Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 57,22262233 (2008). 23 Thorsby, P. M., Midthjell, K., Gjerlaugsen, N., Holmen, J., Hanssen, K. F., Birkeland, K. I.", + "Genome-wide association studies validated these old culprits of T2D and expanded them to include hundreds of single-nucleotide variants (SNVs) that represent more than 150 genomic loci that are associated with T2D, insulin secretion, and insulin resistance [ 11]. Besides TCF7L2 ,PP ARG , and KCNJ11 loci, the most replicated T2D susceptibility variants identied in GWASs were found in and around CDKN2A/2B ,IGF2BP2 ,SLC30A8 ,CDKAL1 and FTO genes [ 1215]. The variants that are most", + "Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008;40:638-45. 20. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their im - pact on type 2 diabetes risk. Nat Genet 2010;42:105-16. 21. Qi L, Cornelis MC, Kraft P, et al. Ge - netic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010;19:2706-15.", + "multiple loci associated with susceptibility to type 2 diabetes, includ- ingTCF7L2 (transcription factor 7-like 2), which had been originally identied by a large-scale association mapping prompted by prior evidence of linkage in that area2,SLC30A8 (solute carrier family 30 member 8), HHEX (haematopoietically expressed homeobox), CDKAL1 (CDK5 regulatory subunit associated protein 1-like 1), CDKN2A/B (cyclin-dependent kinase inhibitor 2A/B) and IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2)37.", + "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 . 74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102. 75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. 76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3]. Obesity is the greatest risk factor for type 2 diabetes mellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF release, free fatty acids, etc.). Both", + "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes n engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in the Botnia subjects (P = 0.006). Of the 16 loci that have been associated with type 2 diabetes previously,8-15 we showed that 11 TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX were associated with an enhanced risk of future" + ], + "answer": "The most common genetic loci associated with an increased risk of Type 2 diabetes include: 1. **TCF7L2** [2], [5], [7] 2. **KCNJ11** [2], [5] 3. **PPARG** [2], [5] 4. **CDKAL1** [2], [5], [7] 5. **CDKN2A/CDKN2B** [2], [5], [7] 6. **IGF2BP2** [2], [5], [7] 7. **SLC30A8** [2], [5], [7] 8. **FTO** [2], [5] 9. **HHEX** [2], [5], [7] These loci have been identified and replicated in multiple studies as being associated with an increased susceptibility to Type 2 diabetes.", + "question": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_20 new file mode 100644 index 0000000..f41bd36 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2016 - The Genomics of Type 1 Diabetes.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2011 - The identification of gene expression.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2011 - The identification of gene expression.pdf" + ], + "extraction_id": [ + "a19924b0-a834-5100-8b24-6b57dcddb82a", + "2aa8d99c-99d7-55de-aa2a-c24a46ea9058", + "919cb859-8f47-5930-8713-090520be523f", + "c4de4c07-4749-5401-bbf3-16988c132852", + "5e43ab7d-3e2b-551c-9a90-f91e970cb8d7", + "312b1856-e1b1-5ae7-8cba-370becf5f7cb", + "e5e4169a-56d8-539f-8ebc-ad44eb75433f", + "c362793d-c70f-5225-afe5-88098042daef", + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "e5e4169a-56d8-539f-8ebc-ad44eb75433f" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "766edfd5-4756-51bf-b636-c94b041d030c", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "4933cdc2-7d36-5181-87c9-63b58498839f", + "470f1f94-792d-5273-a88f-7e06084951c5", + "61558082-f092-5a1d-abbb-a5a81e8a959b", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "61558082-f092-5a1d-abbb-a5a81e8a959b" + ], + "id": [ + "chatcmpl-AIFrXDhQoyphIy4Ti77mFez47y0g5", + "e7d89095-ef66-5c11-982c-879791dd14a4", + "c21caf96-f04a-551d-92b2-f4ff084d43c8", + "fceee048-359b-5854-b45d-5531b9374ce8", + "f4e8a3c8-0b85-5595-8917-933aced8b3ba", + "df21554e-6053-53ae-aae5-e3d1dba1b1f5", + "db06230d-31c0-5947-8c1c-f58c48b6f439", + "e0b86e8e-4e1a-5f6b-9b41-e9a4f912790c", + "cc98a5b9-131e-5b60-919e-82e86b7a37a7", + "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "efd7c210-858d-5125-8da9-46862e19a58a" + ], + "contexts": [ + "by performing a genetic profile on diabetic patients (pharmacogenetics). Furthermore, identification of genetic determinants of diabetic patients will better define the targets of current and future therapies, and will lead to therapies that are more specific for their genetic constitutes. SUMMARY With the advancement of the Human Genome Project, we enter the era of a sequence-based biology. Some progress has been made in the", + "Todate,studiesofdiabeteshaveplayedamajorroleinshapingthinkingabout thegeneticanalysisofcomplexdiseases.Basedontrendsingenomicinformationandtechnology,combinedwiththegrowingpublichealthimportanceofdiabetes,diabetes will likely continue to be an important arena in which methods will bepioneeredandlessonslearned.Itiswithgreatenthusiasmthatwelookforwardtothis effort, and with avid curiosity we await to see whether the lessons of todaywill be supported by the data of tomorrow.", + "DNA code. Therefore, greater unders tanding of the epigenetic basis of disease could enable the 576 discovery new therapeutic targets for the treat ment of numerous human diseases including 577 diabetes and its complications. 578 579 580", + "T ogether, these discoveries will continue to improve our understanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to chronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene - based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "research will contribute positive ly to the life of people living with T1D . Being able pinpoint mutations, and then discover how they contribute to the genetic cause of a condition, can help to open up path s for pharmaceutical treatments. Currently, m ost treatment strategies for genetic disorders do not alter the underlying genetic mutation; but are designed to improve particular signs and symptoms associated with the disorder. For instance, T1D is managed by", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "new therapeutic targets and identify potential diabetic neuropathy biomarkers. The genes identied in the current study conrm datagathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in asingle human tissue sample. Our initial analyses of this data set classied the patient samples based on myelinated bre density and found that two large groups emerged; those with a loss of myelinated bre density 5500 bres/mm", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "the onset and progression of diabetic neuropathy is of primeimportance. The current study takes an important rst step towards this goal by identifying specic sets of genes whose expression accurately classies patient samples with regard to diabetic neuropathy pro- gression and by analysing their interactions within known cellularpathways. Identifying common elements in these complex net-works will yield novel insights into disease pathogenesis, provide" + ], + "answer": "Studying the DNA of people with diabetes helps scientists find better treatments or cures in several ways: 1. **Identification of Genetic Determinants**: By performing genetic profiling on diabetic patients, scientists can identify genetic determinants that define the targets of current and future therapies. This leads to the development of therapies that are more specific to the genetic makeup of individuals with diabetes [1]. 2. **Understanding Disease Mechanisms**: Genetic studies improve our understanding of the biological mechanisms that maintain glucose homeostasis and reveal molecular defects leading to chronic hyperglycemia. This knowledge can lead to the development of more specifically targeted antidiabetic drugs or even gene-based therapies [4]. 3. **Pharmacogenetics**: Pharmacogenetic testing can be used to predict therapeutic responses to different classes of drugs for each patient, allowing for more personalized treatment plans [4]. 4. **Discovery of New Therapeutic Targets**: A greater understanding of the genetic and epigenetic basis of diabetes can enable the discovery of new therapeutic targets, potentially leading to novel treatments for diabetes and its complications [3]. 5. **Stratification of Diabetes Subclasses**: By analyzing DNA variations and their interactions with environmental factors, scientists can stratify type 2 diabetes into subclasses. This stratification allows for more effective treatment strategies tailored to specific genetic and lifestyle interactions [8]. 6. **Identification of Key Genetic Elements**: Genetic studies can identify key genetic elements that determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies. This information helps in identifying novel targets for future interventions [9]. Overall, studying the DNA of people with diabetes provides critical insights that drive the development of more effective and personalized treatments.", + "question": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_3 new file mode 100644 index 0000000..fc94ef5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus.pdf", + "2009 - Cohorts for Heart and Aging Research in Genomic.pdf", + "2014 - Identification of novel risk genes associated with type 1 diabetes mellitus.pdf", + "2020 - Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.pdf", + "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2021 - Genome-wide association studies identify two novel loci.pdf", + "2015 - Genome-wide studies to identify risk factors for kidney disease.pdf", + "2020 - Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease..pdf", + "2009 - Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes.pdf" + ], + "extraction_id": [ + "e2b46a32-6616-55ad-8511-31ee8f9cce45", + "746e7837-d0f3-5a73-bfef-adfd748e35d6", + "4b1681f4-4088-5b15-a704-040e35e31080", + "2c601441-443d-5c47-95bb-6343378dd5dc", + "aa94128a-99f6-59f3-b5fa-33ac97b858d5", + "9369222f-e125-58c0-8f2b-cf5daa867f77", + "fc9812ae-7b35-5dac-af9b-6d60f4faaa54", + "92bd58f8-6770-5c1c-8202-19b08bd57df8", + "2341dbc6-8084-5d51-a52e-f8f667b79bbb", + "0c5401ea-2a43-5578-af0b-6ad1e818fa42" + ], + "document_id": [ + "4b1a56e7-6821-5504-b6da-27dcdf57c6a5", + "9534989a-a5a5-52d8-95b8-0ad2926f228c", + "97fe33b0-a6c7-59b6-bd34-05528e77293f", + "5dd7d700-03db-595d-b1a5-beca77f9579e", + "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "7131256d-7d55-597d-aac5-a62956736923", + "3e696b99-6306-5429-bce9-8d04a2471b2d", + "f0385a45-ad3e-5813-ab1f-b3e227d5164b", + "0fd2b5c8-9bda-5cc8-adb4-231d3842d50f" + ], + "id": [ + "chatcmpl-AIFpJNprqmrM6nedwSTz4Aw1PacbM", + "b6827ec6-aa43-53e3-8d00-19e802bc3010", + "9abaf02e-eee2-504d-be20-d589cb9a3164", + "a1e3ca85-6fd1-5364-87c5-442c3f96ba74", + "263ea999-9662-5518-a606-939f69d09f90", + "53c3668c-95f8-5fb9-b978-e4c03ddfa40f", + "7fd80e84-ec0c-564c-8e8b-278b8c622abb", + "9afcf9a9-3abf-5441-a711-55e25f1ef9b7", + "ad7955f2-824c-59f8-8357-6ee201756ec9", + "5488da5b-5efa-55cd-92c3-a0d77e587fce", + "7f17fa56-1b7a-5d51-a111-3c74b31a5821" + ], + "contexts": [ + "BMC Medical Genomics 2009, 2:72 http://www.biomedcentral.com/1755-8794/2/72 Page 2 of 8 (page number not for citation purposes)Background Genome-wide association study (GWAS) offers unbiased ways to examine association of more than a million singlenucleotide polymorphisms (SNPs) with disease [1]. Sev-eral GWAS have indentified novel genomic regions influ-encing risk for type 2 diabetes mellitus (T2DM) [2-6].However, the challenge remains to prioritize SNPs from", + "GWAS have successfully identified genetic loci associ- ated with a variety of conditions such as type 2 diabetes2 and coronary disease.35The large number of statistical tests required in GWAS poses a special challenge because few studies that have DNA and high-quality phenotypedata are sufficiently large to provide adequate statisticalpower for detecting small to modest effect sizes. 6Meta- analyses combining previously published findings have im-proved the ability to detect new loci.", + "diabetes mellitus6,7. However, the traditional GWAS ignored a large number of loci with moderate effects, because of the strin-gent signi cance thresholds used. Gene-based analysis takes a gene as a basic unit for association analysis. As this method can combine genetic information given by all the SNPs in a gene to obtain moreinformative results 8, it is being used as a novel method com- plementing SNP-based GWAS to identify disease susceptibilitygenes. Notably, this method can increase our chance of nd-", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "that a genome-wide approach could uncover previously unexpected disease pathways. In early 2007, GW AS provided by far the biggest increment to date in our knowledge of the genetics of this common health problem. Six new gene regions identified Together, the six recent GW AS papers provide convincing evidence for six new gene regions involved in type 2 diabetes1621; a seventh publication describes how one of these variants alters BMI and represents by far the best example of an association", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories", + "mechanisms of DR remain poorly understood. A genome-wide association study (GWAS) is a powerful tool to identify genetic loci for complex diseases, and a large number of genetic loci for the susceptibility to various diseases, such astype 2 diabetes, have been successfully identified through GWAS (69). GWAS for DR have been performed, but most of the studies only reported suggestive signals with no replication ( 5)b e c a u s e of their limited sample sizes. Recently, several loci with genome-", + "kidney disease, several loci have been identi ed and validated, but the results were quite heterogenic across different popula- tions and depended on the type of diabetes and stage of disease. The major bene t of GWAS results is to be found in the in- creased understanding of disease mechanism and identi ca- tion of novel pathways and possibly new therapeutic targets.Follow-up studies are important in order to identify variants with speci c biological effect and may provide important", + "Abstract Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally per -", + "linkage or association data. But, none of these studies include in the analysis existing data from GWAs. Finally, a recent study identied additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs.21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds. Thus, by combining data involving 10,128 samples, the authors found" + ], + "answer": "Genome-wide association studies (GWAS) help in identifying diabetes-related genes by examining associations between a large number of single-nucleotide polymorphisms (SNPs) and the disease, such as type 2 diabetes mellitus (T2DM) [1]. GWAS have successfully identified genetic loci associated with T2DM, providing insights into the genetic risk factors and biological pathways underlying the disease [2], [4]. These studies have identified hundreds of SNPs associated with T2DM, which can uncover previously unexpected disease pathways and contribute significantly to our understanding of the genetics of diabetes [5], [9]. Additionally, meta-analyses of GWAS data have improved the ability to detect new loci by combining findings from multiple studies, thereby increasing statistical power [2], [10]. Despite challenges such as the modest effect sizes of individual variants and stringent significance thresholds, GWAS remain a powerful tool for identifying genetic loci for complex diseases like diabetes [7], [10].", + "question": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_4 new file mode 100644 index 0000000..64e0f83 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2011 - Type 1 Diabetes Etiology, Immunology.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2009 - Genetics of Type 1A Diabetes.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf" + ], + "extraction_id": [ + "c584fa40-ecb0-57e0-8bd1-6761ae3b4d27", + "36ba6e4a-46c7-5a84-89ca-2b6e2a3c2a2c", + "508a0315-d23a-506d-bf66-b5c355576f96", + "79b78db5-d77a-51bd-84ff-14b558534df8", + "65402f67-0066-5d04-98c6-5e7b16087495", + "548d7a8d-5314-53f3-b646-108a4b1ac2f2", + "0ed848eb-6e55-5fab-82d0-f10b3c83d98f", + "dd2e688a-2f06-5911-9eb8-13c5d935885e", + "3e3ff61a-86ee-5e2a-9b14-9aa3b06937ff", + "92a54171-9f94-51ea-83cb-11698b1f0c21" + ], + "document_id": [ + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "3c9823cd-3615-53b6-96c8-b7d2123d3eb0", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "7a98f456-6c43-5e9e-b404-31122159eab8", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "3887995f-fa61-5472-b0a2-90b7b39592c2", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "3887995f-fa61-5472-b0a2-90b7b39592c2" + ], + "id": [ + "chatcmpl-AIFpPxGF7qh3CvkVYK1PXW0J3bHPe", + "f654b4ad-31d8-528c-800d-000a0f56438a", + "8480cb40-fb38-5f5e-98c5-41eb2b3bdad2", + "5157af28-d09f-5bbb-8984-61da49bed642", + "5c650a7a-98f1-5119-b66f-5a93db18faec", + "0fa5241d-e039-55b3-ba8c-aa14d0125967", + "f159c8b5-357c-57f6-98e4-5d5436f59925", + "277e2627-b99d-5b35-ae45-1fbaa2bf0710", + "69fb55b3-37a8-5fb6-9916-2ab5be15a0a8", + "3ccbc6c4-a2a3-53ab-b904-c4d5875e2e2c", + "2b1f2a05-4693-595c-94c0-fea40e19539c" + ], + "contexts": [ + "conferred by specic alleles, genotypes, and haplotypes ofthe HLA class II (and class I) genes. There are currentlyabout 50 non-HLA region loci that also affect the type 1diabetes risk. Many of the assumed functions of thenon-HLA genes of interest suggest that variants at theseloci act in concert on the adaptive and innate immunesystems to initiate, magnify, and perpetuate /H9252-cell destruc-", + "II HLA gene associated with type 1 diabetes maps to the 240-kbregion near HLA-B. Diabetes 49: 22172221, 2000. 303. Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF. Localization of type 1 diabetes susceptibility to the MHC class Igenes HLA-B and HLA-A. Nature 450: 887892, 2007. 304. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare variants of IFIH1, a gene implicated in antiviral responses, protectagainst type 1 diabetes. Science 324: 387389, 2009.", + "Although the highly polymorphic HLA class II genesclearly play the most important single role in susceptibilityto type 1 diabetes, variation at these loci alone cannotexplain all of the evidence of genetic association andlinkage of the MHC with type 1 diabetes. To better denegenes within the MHC that may affect type 1 diabetes riskand would therefore merit further studies, the T1DGCundertook a comprehensive study of the genetics of theclassic 4-Mb MHC region. More than 3,000 SNPs and 66microsatellite", + "age to type 1 diabetes in the HLA region and suggestive evidence at a small number of other regions in the genome. In general, the emerging picture from linkage studies is that the class II genes encoding HLA-DR and HLA-DQ, as well as one or more additional genes within the HLA re - gion, confer most of the genetic risk for type 1 dia - betes. Genes outside the HLA region also con - tribute to the risk of type 1 diabetes, but their individual contributions are much smaller than that of HLA.", + "Benkalha and Polychronakos, 2008 ). Other genetic loci ( Table 1) are believed to in uence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a ). 2.1. Human leukocyte antigen (HLA) The association between T1D and the HLA complex was rst de- monstrated in 1973 following observation of an increased frequency ofHL-W15 (HLA antigen) in T1D patients compared to controls ( Singal", + "cyte Antigen (HLA) gene region in immune regulation, and ready availability of serologic markers, led investigators to discover the association between certainHLAalleles and T1D in the early 1970s (33,130,158). The global importance of theHLAonT1Dhassincebeenconrmedingenome-widescansforlinkage:All suchscansperformedtodateshowamajorlocusatthe HLA(28,32,36,78,119). Thefractionofallgeneticrisk,whichcanbeattributedtothecontributionof HLA genes to T1D susceptibility, is about 44%, with a Sof3.4 (160).", + "The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all dis- orders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 andHLA-DRB1 (refs 13), but these genes cannot completely explain the association between type 1 diabetes and the MHC region411.Owing to the regions", + "The HLA class I A locus a ects susceptibility to type 1 diabetes. Hum. Immunol. 63, 657 664. pii). https://doi.org/S0198885902004214 . Noble, J.A., Valdes, A.M., Cook, M., Klitz, W., Thomson, G., Erlich, H.A., 1996. The role of HLA class II genes in insulin-dependent diabetes mellitus: molecular analysis of 180 Caucasian, multiplex families. Am. J. Hum. Genet. 59, 1134 1148 . Noble, J.A., Valdes, A.M., Thomson, G., Erlich, H.A., 2000. The HLA class II locus DPB1", + "to type 1diabetes susceptibility, including within the MHC itself.Currently, there are over 50 non-HLA regions that signi-cantly affect the risk for type 1 diabetes (http://www.t1dbase.org). Many of these regions contain interesting,but previously unrecognized, candidate genes. A few re-gions contain genes of unknown function or no knownannotated genes, suggesting roles for long-distance generegulatory effects, noncoding RNAs, or unknown mecha-nisms. Against a background of ever-improving knowledgeof the", + "the 240-kb region near HLA-B. Diabetes 49,22172221 (2000). 6. Lie, B. A. et al. The predisposition to type 1 diabetes linked to the human leukocyte antigen complex includes at least one non-class II gene. Am. J. Hum. Genet. 64, 793800 (1999). 7. Valdes, A. M. et al. Extended DR3 D6S273-HLA-B haplotypes are associated with increased susceptibility to type 1 diabetes in US Caucasians. Tissue Antigens 65,115119 (2005). 8. Valdes, A. M., Erlich, H. A. & Noble, J. A. Human leukocyte antigen class I B and C" + ], + "answer": "The HLA region plays a significant role in the genetic predisposition to Type 1 diabetes. The major genetic susceptibility determinants for Type 1 diabetes have been mapped to the MHC class II genes, specifically HLA-DQB1 and HLA-DRB1 [7]. These genes are part of the highly polymorphic HLA class II genes, which play the most important single role in susceptibility to Type 1 diabetes [3]. The class II genes encoding HLA-DR and HLA-DQ, along with one or more additional genes within the HLA region, confer most of the genetic risk for Type 1 diabetes [4]. The contribution of HLA genes to Type 1 diabetes susceptibility accounts for about 44% of the genetic risk [6]. However, variation at these loci alone cannot explain all of the genetic association and linkage of the MHC with Type 1 diabetes, indicating that other genes within the MHC region may also affect Type 1 diabetes risk [3].", + "question": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_5 new file mode 100644 index 0000000..db215c5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "2005 - Type 2 diabetes mellitus from genes to disease.pdf", + "2016 - Association of genetic variants in INS (rs689), INSR (rs1799816) and PP1G.G (rs1799999) with type 2 diabetes (T2D) a case\u2013control study in three ethnic groups from N.pdf", + "2007 - Bioethnic Conscription Genes, Race.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2011 - Dating the age of admixture via wavelet.pdf", + "2020 - Precision Medicine in Diabetes.pdf", + "2014 - Diabetes in Europe An update.pdf", + "2016 - TRPV1 Gene Polymorphisms Are Associated with Type 2 Diabetes by Their Interaction with Fat Consumption in the Korean Genome Epidemiology Study.pdf" + ], + "extraction_id": [ + "61fb4dd8-1428-5add-8c41-9ec2459ffd5a", + "090365f1-32e0-5adc-b589-b9331e0630a0", + "73278198-67af-5556-9414-86580dd07c48", + "4cbd4dfc-da8e-5432-b844-5f70d6f3811d", + "95f0e6f8-da7d-5997-ab8a-a1aad020c706", + "8d323598-fdf7-56cf-8290-be85929f0eaf", + "a5c137e5-84d2-5d75-8191-fa6b0be3d39e", + "9dc25bb6-787b-5e7a-af5d-d1353d122959", + "fa58324a-e5b7-538e-9cbb-0549887a2154", + "8276c974-f60b-5f59-943d-94a635160d1d" + ], + "document_id": [ + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "52687a38-6a4b-51d2-aafa-812c76981dfe", + "5fe7c5f4-a209-56be-8504-c08073335c3b", + "d90126d9-fd87-5b38-87f7-08415f690836", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "786cebc5-c3cc-586e-bdc0-e7bee67edc19", + "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "81e1fc53-6768-590f-9b47-9a5105b6ddb5", + "521db985-2ce8-56c3-aed7-b38ef41cce45" + ], + "id": [ + "chatcmpl-AIFpUuEUTWxzzcta8xK3fjxfSUNPx", + "49748fe8-4351-5cd1-8367-957a160a59d9", + "80ad1f9c-4f67-5a68-9446-1f692b23f324", + "5fd9c60a-410f-5782-90a9-03d377a5f72b", + "d02a16ce-c62e-537d-9d32-266018c70415", + "684d1e26-b78a-5dde-b405-a79ee28087c3", + "8445ab0a-2287-5537-ab3a-cb058205e944", + "10c1db42-f724-5885-99e0-7637dfce63ca", + "d29cdd31-d214-52cf-b236-be4de1182b26", + "6fd138d2-6960-55fd-b656-05f4e84a0c6d", + "2771c343-be7b-51a2-a598-235647357416" + ], + "contexts": [ + "of diabetes when compared to the native population while not necessar-ily different from populations where they origi-nate from. Risk factors for diabetes appear to be similar between populations, mostly insulin resistance, obesity, and sedentary lifestyle with possible genetic differences contributing to the increased susceptibility. Some data suggest a greater prevalence of microvascular complica-", + "nants of type 2 diabetes between immigrant and native populations. Some studies in South Asian (Indian) populations suggest that genetic differ-ences may exist [ 17 , 30 ], but larger studies are needed to get better insight into this issue. Prevalence Estimates The prevalence of diabetes in minorities is affected by ethnicity and country of residence. In one study in the UK [ 59 ], standardized preva-", + "majority of cases it is difficult to replicate the findingsin other populations. One of the major problems in thesearch for genes responsible for common forms ofdiabetes is the genetic heterogeneity of the diseasewith different genes responsible for the developmentof T2DM in different populations. Furthermore, evenwithin the same ethnic group, different genes may beresponsible for different subtypes of diabetes (for in-stance with predominating failure in insulin secretionor insulin resistance). This is", + "across different races or populations but show ethnicity- specific differences. The pathogenesis of T2D involves genetic variants in the candidate genes. The interactions between the genes involved in insulin signaling and secre - tory pathways are believed to play an important role in determining an individuals susceptibility towards T2D. Therefore, the present study was initiated to examine the differences, if any, in the contribution of polymorphisms", + "That is, the minute genetic differences discernable with SNPs, patterns of single nu-cleotides (A,G,T ,C), and other mutation analysis technologies are now used to explainpatterns of disease between populations, which are in turn understood as the basisfor biological differences between the populations themselves. The case of diabetesgenetics research affords a more nuanced look at what is labeled genetic determinism.It is evident in diabetes research that SNPs and haplotypes, (an inherited pattern of 99", + "- tion for disease classification. This genetic component may be specifically important when understanding the pathogenesis of diabetes in ethnic groups, when BMI [14, 15] and HbA1c [16] show distinct differences between ethnicities. Though applying patient-matched, genomic information is currently unrealistic for disease diagnosis, it may hold the key for revealing commonalities across ethnic and demographic groups when classifying diabetic onset, progression, and severity.", + "particularly useful for understanding differences in dis-ease prevalence and drug response among differentpopulations. There is ample evidence that human popu-lations have different susceptibility to diseases, exhibit-ing substantial variation in risk allele frequencies [1].For example, genetic predisposition to asthma differsamong the differentially-admixed Hispanic populations of the United States, with the highest prevalence observed in Puerto Ricans. Ge netic variants responsible", + "populations and across countries. World-wide differences in prevalence of theforms of diabetes necessitates inclusion of currently understudied populationsfor the development of precision diag-nostics and therapeutics. As a result, theprecise subtype of diabetes a particularindividual is diagnosed with may vary indifferent populations based on subtypefrequency or genetic or dietary or life-style differences. The communication strategy used by the interventionalist and the patient s", + "were positively associated with country level income [49]. However, the drivers for the observed pattern with geographi- cal differences and varying time trends are still unclear. Susceptibility to type 1 diabetes denitely has a strong genetic component (HLA genotype) [50], but the heterogeneity of type 1 diabetes cannot be explained solely by the prevalence of susceptibility genes [5153] . Thus, the reasons for changes in", + "twice higher than that of 2010 [3] . The genetic influences on the prevalence of type 2 diabetes i n the Asian population are mainly related to insulin secretion capacity [4] ; other genes involved in the risk of type 2 diabetes are not substantially different in other ethnic groups [5] . The most relevant genes contributing to ethnic differences are associated with insulin secretion capacity, and they are" + ], + "answer": "Genetic differences contribute to variations in diabetes prevalence among different populations in several ways: 1. **Genetic Heterogeneity**: Different genes may be responsible for the development of type 2 diabetes mellitus (T2DM) in different populations, indicating genetic heterogeneity. Even within the same ethnic group, different genes might be responsible for different subtypes of diabetes, such as those with predominating failure in insulin secretion or insulin resistance [3]. 2. **Ethnicity-Specific Genetic Variants**: The pathogenesis of T2DM involves genetic variants in candidate genes, and these variants can show ethnicity-specific differences. The interactions between genes involved in insulin signaling and secretory pathways play a crucial role in determining an individual's susceptibility to T2DM [4]. 3. **Single Nucleotide Polymorphisms (SNPs) and Haplotypes**: Minute genetic differences, such as SNPs and haplotypes, are used to explain patterns of disease between populations. These genetic markers are understood as the basis for biological differences between populations, which can affect diabetes prevalence [5]. 4. **Risk Allele Frequencies**: Human populations exhibit substantial variation in risk allele frequencies, which contributes to different susceptibilities to diseases, including diabetes, among populations [7]. 5. **Insulin Secretion Capacity**: In some populations, such as the Asian population, genetic influences on the prevalence of type 2 diabetes are mainly related to insulin secretion capacity. This suggests that genes associated with insulin secretion capacity are particularly relevant in contributing to ethnic differences in diabetes prevalence [10]. Overall, genetic differences, including variations in specific genes, SNPs, and risk allele frequencies, contribute to the observed variations in diabetes prevalence among different populations. These genetic factors interact with environmental and lifestyle factors to influence diabetes risk.", + "question": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_6 new file mode 100644 index 0000000..656ba76 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2006 - Adiponectin, type 2 diabetes and the metabolic syndrome.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2010 - The Genetics of Type 2 Diabetes.pdf", + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2018 - Association of PGC-1\u03b1 gene with type 2 diabetes in three unrelated endogamous groups of North-West India (Punjab) a case-control and meta-analysis study.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "2013 - Gene-Environment and Gene-Treatment.pdf", + "2018 - Refining the accuracy of validated target identification through coding variant.pdf" + ], + "extraction_id": [ + "4647b43a-e4a0-5e8a-9cf5-6bf33cd6e672", + "2d610953-ea5c-5c01-ad19-60c607383da4", + "1df8f645-85c4-5832-8142-09bacafcd01d", + "f8b79de5-3e0c-5495-b6c2-8a3be6138223", + "94ee1317-d606-5921-8175-a86da2fa95d6", + "02cdfa1b-cc8f-5141-bde0-1079d252c6e8", + "4bdd6cdb-1f2a-585f-b08e-392a54c6dad8", + "2d610953-ea5c-5c01-ad19-60c607383da4", + "a6b92963-2cf0-51a4-8686-ce3a7515d443", + "d96545e5-f3a0-5765-9b06-27a41219d3b9" + ], + "document_id": [ + "6a46f7cf-e75b-5b72-b77b-7e0cc03f92d8", + "4ea83190-476d-5090-a461-abde1adccbc5", + "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "2faa21d2-146e-568a-b881-63201819e99a", + "4ea83190-476d-5090-a461-abde1adccbc5", + "4ea83190-476d-5090-a461-abde1adccbc5", + "4ea83190-476d-5090-a461-abde1adccbc5", + "fe958fb1-5408-56ec-b102-ccf07b4bac2d", + "3362e616-f824-55fa-9b4d-3ee8dcf52ac0" + ], + "id": [ + "chatcmpl-AIFpdRa9QE6LvKot0urXoNDlpAF5x", + "6f046969-4e26-5dee-a310-cf32dc1f799c", + "c909cc5c-6fdb-5646-8332-973a92ac9486", + "c3ac7ed2-1b42-5c87-9104-b6da2e33b30b", + "02a160ba-95ee-5aa9-bc45-445b4706715b", + "4e415210-bf41-542f-841c-4bb17622d2e6", + "8d7fb270-e23f-5d89-b75c-50b8fbd22fe8", + "9f62a8cf-a14f-5989-a899-cf1f525905bf", + "818c1d6b-c1c1-570d-9e7a-87449fae279a", + "793e2430-fa2b-513a-a4ab-0c85a167de3f", + "7c375d6d-672d-594c-a56e-7391ed3e9daa" + ], + "contexts": [ + "The transcription factor peroxisome-proliferator- activated receptor gamma (PPAR g) is known to inuence insulin sensitivity, and acts partly via amodulation of the circulating adiponectin level (PPAR gagonists increase the adiponectin level) (Ref. 38). The PPAR gP12A SNP is a well- established genetic variant that modulates insulin sensitivity and the risk of type 2 diabetes (Ref. 39). In a Chinese family study, Yang et al.demonstrated a genetic interaction between the", + "intricate regulation of PPAR signaling to pave the way to tailored therapies in patients with insulin resistance and T2D. Keywords PPARG genetic variants .Dominant-negative isoforms .Post-tranlational modifications .Adipose tissue dysfunctions .Drug responsiveness .Type 2 diabetes Introduction Peroxisome proliferator activated receptor gamma (PPAR ) is a ligand-activated transcription factor belonging to the nu-", + "2 . A widespread Gly482Ser polymorphism of PGC1 - (known as PPARGC1 ), a transcriptional coactivator of a series of nuclear receptors includ-ing PPARG , has been associated with a 1.34 genotype relative risk of T2DM [93] . In this study, a test for interaction with the Pro12Ala variant in PPARG gave no indication for additive effects on diabetes status. Other genes have been shown to be implicated in the genetic", + "PPARG Peroxisome proliferator-activated receptor- gene. This gene is located on chromosome 3p25, and has been studied as a candidate genefor type 2 diabetes based on its role in adipocyte and lipid metabolism. The Pro12Ala variant in particular has been associated with adecrease in insulin sensitivity and a several-fold increased risk of type 2 diabetes. PPAR is a target for the thiazolidinedione class of oralantidiabetic agents", + "Genetic variation in the peroxisome proliferator-activated receptor (PPAR) and peroxisome proliferator-activated receptor gamma co-activator 1 (PGC1) gene families and type 2 diabetes. Ann Hum Genet 78:2332 Vimaleswaran KS, Radha V, Ghosh S, Majumder PP, Deepa R, Babu HN etal (2005) Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to type 2 diabetes in Asian Indians. Diabetic Med 22:15161521", + "Dali-Youcef N, et al. The Pro12Ala PPARgamma2 variant deter- mines metabolism at the gene-environment interface. Cell Metab. 2009;9:88 98. 53. Agostini M, Schoenmakers E, Mitchell C, Szatmari I, Savage D, Smith A, et al. Non-DNA binding, dominant-negative, human PPARgamma mutations cause lipodystrophic insulin resistance. Cell Metab. 2006;4:303 11. 54. Agostini M, Gurnell M, Savage DB, Wood EM, Smith AG, Rajanayagam O, et al. Tyrosine agonists reverse the molecular", + "associated with a marked increase in T2D risk in the general population, schematized in Fig. 1. The latter systematically tested all the possible PPAR protein variants by using a large-scale pooled functional assay based on a human macro- phage cell line. Using these in vitro data to train a classifier by supervised machine learning, they identified six pathogenic PPARG variants (R194Q, A417V, R212W, P387S, M203I, and T356R) in patients with partial lipodystrophy [ 109].", + "lipid metabolism, as well as insulin sensitivity and inflammatory pathways. These pleiotropic functions confer great relevance to PPAR in physiological regulation of whole-body metabolism, as well as in the etiology of metabolic disorders. Accordingly, PPARG gene mutations, nucleotide variations, and post-translational modifications have been associated with adipose tissue disorders and the related risk of insulin resistance and type 2 diabetes (T2D). Moreover, PPAR alternative splicing isoforms", + "the PPARgamma locus. Diabetes 2001;50:686 689 12. Kahara T, Takamura T, Hayakawa T, et al. PPARgamma gene polymorphism is as-sociated with exercise-mediated changes of insulin resistance in healthy men. Me- tabolism 2003;52:209 212 13. Franks PW, Luan J, Browne PO, et al. Does peroxisome proliferator-activated receptor gamma genotype (Pro12ala) modify the association of physical activityand dietary fat with fasting insulin level? Metabolism 2004;53:11 16 14. Memisoglu A, Hu FB, Hankinson SE, et al.", + "30. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci USA 111, 1312713132 (2014). 31. Majithia, A. R. et al. Prospective functional classification of all possible missense variants in PPARG . Nat. Genet. 48, 15701575 (2016). 32. Claussnitzer, M. et al. Leveraging cross-species transcription factor binding" + ], + "answer": "The PPAR-gamma gene encodes a transcription factor that plays a crucial role in regulating insulin sensitivity and lipid metabolism, which are important in the context of diabetes [4]. PPAR-gamma influences insulin sensitivity partly by modulating circulating adiponectin levels, with PPAR-gamma agonists known to increase these levels [1]. This gene is a target for the thiazolidinedione class of oral antidiabetic agents, which are used to improve insulin sensitivity in patients with type 2 diabetes [4]. Variants of the PPAR-gamma gene can significantly impact the risk and progression of type 2 diabetes. The Pro12Ala variant, for example, is a well-established genetic variant that modulates insulin sensitivity and increases the risk of type 2 diabetes [1], [4]. Other pathogenic PPARG variants, such as R194Q, A417V, R212W, P387S, M203I, and T356R, have been identified in patients with partial lipodystrophy and are associated with an increased risk of type 2 diabetes [7]. These variants can affect the gene's function in adipocyte differentiation and metabolism, thereby influencing the development of insulin resistance and type 2 diabetes [10].", + "question": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_7 new file mode 100644 index 0000000..485f8f6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf" + ], + "extraction_id": [ + "57736895-897e-54e5-a735-aadcbd77cb63", + "5f8a0ddd-a0c7-5151-9b6a-e0980bb94aa6", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "0a3e3095-4789-505a-96b7-123a05078e95", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "4e73f54b-d265-594d-9fc1-9535a2d84672", + "a36cee80-5961-55e5-8ea4-8d4e1bc501a9", + "62d513ed-2dca-5f45-9da2-d847f92fc931" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "eee2f79d-e093-52fb-871a-798fd859235e", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6" + ], + "id": [ + "chatcmpl-AIFppDyOUKllFXSAk1UvPBBd5ythq", + "f42c0f84-d2a8-5bf9-89c2-3dd182bfb235", + "1859f32b-8f5c-5c3c-9f4d-54193d37645d", + "df30dab3-a490-5497-a079-2741f9039f87", + "eadf2320-de70-5499-ade0-7aa9930ac091", + "99ccc9a2-865f-5d11-9b08-b26261d02fc9", + "1f114642-3f77-5346-89e8-394c433f66ff", + "57b9550d-0258-5a87-be57-976f471e5763", + "4b170851-2dbd-5c06-9e3a-188d30a00170", + "83053df5-47ac-59da-9c30-69740a64372d", + "6f0adc7f-54ce-5a70-a2ea-153e074ccbdf" + ], + "contexts": [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "CRISPR-Cas9 epigenome editing enables high-throughput screening for functionalregulatory elements in the human genome. Nature Biotechnology 35(6):561 e568. [58] Hodson, D.J., Mitchell, R.K., Marselli, L., Pullen, T.J., Gimeno Brias, S., Semplici, F., et al., 2014. ADCY5 couples glucose to insulin secretion in humanislets. Diabetes 63(9):3009 e3021 . [59] Zhou, Y., Park, S.-Y., Su, J., Bailey, K., Ottosson-Laakso, E., Shcherbina, L.,", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19", + "DNA donors as templates, it is possible the nCas9-RT will beable to convert all variants at once. This new technique, however,is still in early development, and its editing ef ciency and side- effects remain to be seen.FUTURE PROSPECTIVES Recent technological developments around CRISPR-Cas9 and itsderivative technologies, combined with advances in humancellular models, should accelerate our understanding of theinterplay between diabetes risk-associated genetic variants and" + ], + "answer": "CRISPR/Cas9 technology can be used to study or treat genetic forms of diabetes in several ways: 1. **Genome and Epigenome Editing**: CRISPR/Cas9 has revolutionized the ability to modify genomes and epigenomes, allowing researchers to target specific genes associated with Type 2 Diabetes (T2D) and manipulate cis-regulatory elements related to beta cell function and glucose homeostasis [1]. 2. **Gene Knockout Studies**: CRISPR has been used to knock out genes by introducing frameshift mutations, which result in protein depletion. This approach has been adopted to study several genes in beta cell lines and human embryonic stem cell-derived beta cells, providing insights into the genetic basis of diabetes [4]. 3. **Correction of Point Mutations**: CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method for this is CRISPR/Cas9-based homology-directed repair (HDR), which involves generating a Cas9-mediated cut near the mutation site and using a homologous donor template to introduce the intended nucleotide change [5]. 4. **Disease Modeling**: CRISPR/Cas9 can be employed in human pluripotent stem cells (hPSCs) to model diabetes and metabolic syndromes in vitro. This allows researchers to study the disease mechanisms and test potential treatments in a controlled environment [3]. 5. **Animal Models**: CRISPR/Cas9 has been used to create knockout and knock-in mouse models, such as leptin and leptin receptor knockout mice, which serve as tools for diabetes and obesity research. These models help in understanding the genetic factors contributing to diabetes and testing therapeutic interventions [7]. Overall, CRISPR/Cas9 technology provides a powerful platform for both studying the genetic underpinnings of diabetes and developing potential gene therapies to treat the disease.", + "question": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_8 new file mode 100644 index 0000000..278b7cf --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2004 - Diabetes Genes a.pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2007 - Physical activity modifies the effect of SNPs in the SLC2A2 (GLUT2).pdf", + "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "2009 - Zinc and Diabetes - clinical links and molecular mechanisms.pdf", + "2020 - Genetics and Epigenetics New Insight on Gestational Diabetes Mellitus.pdf", + "2012 - Reduced Insulin Exocytosis in Human Pancreatic b-Cells.pdf", + "2000 - A High Fasting Plasma Insulin Concentration.pdf", + "2006 - Polymorphisms in the Ghrelin Gene Are Associated with Serum High-Density Lipoprotein.pdf", + "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf" + ], + "extraction_id": [ + "0734af87-4854-5a0f-b10c-2ea89376cb87", + "78e2a11a-4e89-5d14-b076-ef24c92b35b2", + "276a7b90-6325-59c8-b8b2-77f855aa2553", + "51702d4a-735b-5bc4-98a4-d26bf1e58b40", + "a482defd-8d6a-5966-8ec1-5aa7e49c14f1", + "7d315f2c-43f0-587a-9370-e0f205d6c611", + "e6e7fc9f-e4a4-5d51-9070-01ce34cffcd3", + "6aefb64e-b732-5742-90a4-f2aa43c8b866", + "00f6985d-f69b-50e3-b673-0ec508e6c025", + "a228ec1a-de5b-5e0c-b24f-db8249be4053" + ], + "document_id": [ + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "6f5ced46-b777-563a-b644-432f4e7e2644", + "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "72ab8458-928b-56b9-9547-1ba4b59dfab9", + "301a7093-a33a-57c9-8979-58146c57ced1", + "689e4fcc-99db-5798-8f1d-68c41d4638db", + "77375e8f-ca8e-5bbb-b310-910dd82ade9c", + "8ad4e1b5-9c29-5b20-bed8-afbf4a14862a", + "3b301dd1-17bd-5632-9a96-d6294c6d7650" + ], + "id": [ + "chatcmpl-AIFq1suHUy36hH4x2WAKqkv1fOq53", + "bdc8f1de-8c90-5e28-8a21-a5bb0182cfe1", + "bc93539a-df5f-5720-a4ce-0345fe4b66d4", + "0a7e6fb3-bf85-5440-adeb-c66fca9d170a", + "a4973968-2510-5f08-8252-f2be85be3c42", + "4bab532c-8b73-54b8-905a-d7b070af1da8", + "42eee55f-adfa-5a04-b3f0-a592b3b08a2c", + "65c8f702-eee5-550e-bd63-78892b158c93", + "a6ef60db-d564-5f55-a31a-db893879ab14", + "b91a2e1b-eb4d-5e1b-a85c-46a8f394603c", + "277f37e3-ee45-5619-b051-33d5ba95bd07" + ], + "contexts": [ + "Effectors Glucose transporters. A number of polymorphisms have been identified in the GLUT4 gene. None of them have been linked to or found to be associated with type 2 diabetes in a variety of populations. 5960 Interestingly, an association was found between a polymorphism in the human GLUT! gene and type 2 diabetes60 that was significant for obese women. Regulation of GLUT4 protein expression in diabetes occurs in a strongly tissue-specific", + "M,XiangKS,etal.1996.Geneticcontri-bution of polymorphism of the GLUT1and GLUT4 genes to the susceptibilityto type 2 (non-insulin-dependent) dia-betes mellitus in different populations.Acta Diabetologica 33:19397 141. Poulsen P, Kyvik KO, Vaag A, Beck- Nielsen H. 1999. Heritability of type II(non-insulin-dependent) diabetes melli-tus and abnormal glucose toleranceapopulation-basedtwinstudy. Diabetolo- gia42:13945 142. Pugliese A, Zeller M, Fernandez AJ,", + "A mutation in the Glut2 glucose transporter gene of a diabetic patientabolishes transport activity. J Biol Chem 269: 1776517767, 1994. 36.Patel P, Bell GI, Cook JT, Turner RC, Wainscoat JS. Multiple restriction fragment length polymorphisms at the GLUT2 locus: GLUT2haplotypes for genetic analysis of type 2 (non-insulin-dependent) diabetesmellitus. Diabetologia 34: 817821, 1991. 37.Pereira MA, FitzerGerald SJ, Gregg EW, Joswiak ML, Ryan WJ, Suminski RR, Utter AC, Zmuda JM. A collection of Physical Activity", + "NootherrecentassociationsofpolymorphismswithT2Dhavebeenreplicated to date (Table 5). However, a recent meta-analysis (106) identied some earlyreproducibilityofanassociationbetweenvariationin GLUT1andT2D,originally reportedin1988(104).Itislikelythatthisassociationhasnotbeenpursuedfurtherfor several reasons, but one possibility is a study that reported the rejection oflinkageto GLUT1athighlevelsofsignicance(46).However,linkagehaslimited", + "mechanism by which type 2 diabetes is influenced remains to be identified. There have been several attempts to clarify the role of the polymorphism in SLC30A8 in the development of type 2 diabetes and the focus has been set on insulin secretion dueto the importance of ZnT-8 for insulin storage in the granulaof pancreatic cells. The results are controversial, but there appears to be an association between the risk variant of rs13266634 and reduced insulin secretion. Interestingly, decreased insulin", + "glucose tolerance, suggesting a r ole for this polymorphism in the onset of GDM as well as type 2 diabetes mellitus ( 17). The switch on IRS-1 of the amino acid GLY972 Arg (rs1801278) impairs insulinsecretion, and a study on 1306 GDM patients and 1973 pregnantwomen without GDM found a signi cant association between the presence of this polymorphism and the risk of GDM ( 18). Intriguing results were generated by a study on the genetic", + "tients the EUGENE2 study. Diabetologia 2008;51:816 820 32. Kirchhoff K, Machicao F, Haupt A, et al. Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated with impaired proinsulinconversion. Diabetologia 2008;51:597 601 33. Nicolson TJ, Bellomo EA, Wijesekara N, et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 2009;58:2070 2083", + "is markedly reduced in glucose-unresponsive islets from ani-mal models of type 2 diabetes (51). In a previous study in PimaIndians, we found that ~5% of this population carries a mis-sense polymorphism in exon 3 of the GLUT2 gene (52), but this polymorphism was not associated with the residual fast-ing plasma insulin concentration in the present study.Despite the fact that GLUT2 is an attractive candidate, it", + "polymorphisms in 24 DNA samples. Common variants were thengenotyped in 760 type 2 diabetic patients and 641 nondiabetic sub-jects. Genetic associations with diabetes-related phenotypes werealso analyzed. Results: Nine polymorphisms were identified, and four common poly- morphisms [g. /H110021500C /H11022G, g./H110021062G /H11022C, g./H11002994C/H11022T, g./H11001408C/H11022A (Leu72Met)] were genotyped in a larger study. The genotype distri-butions of these four common polymorphisms in type 2 diabetes pa-", + "in turn, result in a defective or poorly expressed glucagonprotein and lead to decreased insulin secretion and conse- quently hyperglycaemia [ 48]. The current study identified, for the first time, several type 2 diabetes-associated risk alleles associated with a higher riskof GDM, namely rs7957197 ( HNF1A ), rs10814916 ( GLIS3 ), rs3802177 ( SLC30A8 ) and rs7041847 ( GLIS3 ). These SNPs" + ], + "answer": "Genetic polymorphisms in the GLUT4 gene have been studied to determine their significance in relation to Type 2 diabetes. However, according to the provided context, none of the identified polymorphisms in the GLUT4 gene have been linked to or found to be associated with Type 2 diabetes across various populations [1]. This suggests that, despite investigations, there is no established connection between GLUT4 gene polymorphisms and the susceptibility to Type 2 diabetes.", + "question": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_9 new file mode 100644 index 0000000..d6dd48e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_diabetes_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2023 - Genetic Link Between Type 2 Diabetes Mellitus and Cardiovascular Disease.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2009 - Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.pdf", + "2013 - The miRNA Profile of Human Pancreatic Islets and BetaCells and Relationship to Type 2 Diabetes Pathogenesis.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "2018 - MicroRNA profiling and their pathways in South African.pdf", + "2009 - Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.pdf", + "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf" + ], + "extraction_id": [ + "2211fc04-119d-534b-8de8-dfa4d1bfbf09", + "b1d2c95c-d639-5c75-8c52-278f1e187675", + "7d22ecdf-dd9f-53e9-aa2b-df81bd03c3bc", + "65ad21df-f728-54b6-b329-9ed8793c33ce", + "593dfb70-8b55-5a74-abd5-446394a0bd23", + "0cb154ce-660d-54fa-a31f-0391434a5470", + "14577d73-d320-54dd-93f2-c55f986bc8bc", + "42c407dd-9f88-57b3-b47b-e467c486e3a4", + "767d65c7-b99d-5427-8f5a-4afa10669e11", + "9e010393-b98f-5f6c-a62d-fc0646ba8667" + ], + "document_id": [ + "c54f9f64-7e6d-5186-a1de-d487ba9d19b8", + "766edfd5-4756-51bf-b636-c94b041d030c", + "c3d2aced-4550-553f-abed-0d3a7ac1414f", + "05e7f076-6b4a-5ab0-b4d0-28e4b6eeef8f", + "470f1f94-792d-5273-a88f-7e06084951c5", + "ce4f171c-494c-53f2-a770-c3edd3561c40", + "b6bb090d-7176-59db-af04-582aa1d5cf10", + "c3d2aced-4550-553f-abed-0d3a7ac1414f", + "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2" + ], + "id": [ + "chatcmpl-AIFq5UdPTHMiHxXb8m3RyqvBs55fY", + "c2fa8cbd-5f7f-5086-90ec-d1e5e6df0ee9", + "3cd8facc-0c2c-5a48-9f7c-cbd5685d914a", + "f35c5082-c877-5cdf-9ba8-a91dd72da2e8", + "abbcafb6-f502-5648-a9a4-196466452564", + "8347a530-d264-5d7a-81f6-704f8ed7bf57", + "f0bb404a-2062-584e-850d-cf49a1e0b4a7", + "a9695ed0-6f3d-5e79-ab99-514119637e0b", + "1d9d150b-27f9-55f7-8111-1f6de79a78bc", + "5bf6de7b-8b41-5a32-a513-843f0f71c640", + "01d78f49-9996-58ea-b076-e352ff22461c" + ], + "contexts": [ + "MicroRNAs (miRNA) ar e single -stranded, small RNA molecules that act at the post - transcriptional standard to regulate their target or source genes. Many biological processes are regulated by this Micro RNA. Since its discovery about two decades ago. It is correlated with a com prehensive set of diseases and described by numerous miRNAs, including T2DM and cardiovascular diseases. Specifically, with respect to T2DM, micro RNA plays a", + "they can act as oncogenes or tumor suppressors (8, 29, 72). miRs are associated with the 341 regulation of genes relevant to insulin secre tion, cholesterol biosynthesis, fat metabolism and 342 adipogenesis, crucial pathways in the pathogene sis of diabetes (53, 114, 115). miRs have also 343 been implicated in TGF- signaling related to th e pathogenesis of diabetic nephropathy with key 344 miRs such as miR-192, miR-216a, miR-217 and miR-377 being up-regula ted in glomerular 345", + "Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM et al (2005) Microarray analysis shows that some microRNAs down-regulate large numbers of target mRNAs. Nature 433:769773 Lovis P, Roggli E, Laybutt DR, Gattesco S, Yang JY et al (2008) Alterations in microRNA expression contribute to fatty acid-induced pancreatic beta-cell dysfunction. Diabetes 57:27282736 Nadler ST, Stoehr JP, Schueler KL, Tanimoto G, Yandell BS et al", + "Abstract Recent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell. MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose", + "evidence demonstrates that miRNAs and lncRNAs can alsoregulate the expression of genes and modulate the actions of growth factors and inflammatory factors related to diabetic complications [ 8]. These reports have been described in sev- eral reviews [ 8,8791] and are only briefly discussed here. Numerous recent reports have demonstrated abnormal ex- pression of various miRNAs in renal, vascular and retinal cellsunder diabetic conditions, and in vivo models of related", + "In addition, miRNAs have been shown to be involved in T2DM. For example, miRNAs play major roles in pancreatic islet development, cell dysfunction, insulin synthesis and secretion and insulin resistance [148] . Studies based on miRNA microarray analysis have identified many different miRNAs involved in the pathology of both T1DM and T2DM; these miRNAs include mi R-375, miR -29, miR -9, miR-124a, miR -195, miR -222, miR -126, miR -133a, miR -296, miR -96, miR -34a, miR -146b, miR -657,", + "26. He Y , Ding Y , Liang B, Lin J, Kim TK, Yu H, Hang H, Wang K. A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus. Int J Mol Sci. 2017:18. 27. Dias S, Hemmings S, Muller C, Louw J, Pheiffer C. MicroRNA Expression Varies according to Glucose Tolerance, Measurement Platform, and Biological Source. Biomed Res Int. 2017;2017:1080157. 28. El Ouaamari A, Baroukh N, Martens GA, Lebrun P, Pipeleers D, van Obberghen E. miR-375 targets 3'-phosphoinositide-dependent protein kinase-1 and", + "nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand therelationship between miRNA regulation and obesity- induced diabetes, we quantitatively proled approximately220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs proled wereexpressed in all three tissues, with many miRNAs in each tissue showing signicant changes in response to genetic", + "11. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281 97. 12. Pirola L, Balcerczyk A, Tothill RW, et al. Genome-wide analysis distinguishes hyperglycemia regulated epigenetic signatures of pri- mary vascular cells. Genome Res. 2011;21(10):1601 15. 13.Cooper ME, El-Osta A. Epigenetics: mechanisms and implications for diabetic complications. Circ Res. 2010;107(12):1403 13.Thispaper also provides a review of evidence pertaining to the role", + "128. Diao X, Shen E, Wang X, Hu B. Differentially expressed microRNAs and their target genes in the hearts of streptozotocin-induced diabetic mice. Mol Med Rep (2011) 4:63340. doi:10.3892/mmr.2011.489 129. La Sala L, Cattaneo M, De Nigris V , Pujadas G, Testa R, Bonfigli AR, et al. Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells. Cardiovasc Diabetol (2016) 15:71. doi:10.1186/s12933-016-0390-9" + ], + "answer": "MicroRNAs (miRNAs) regulate gene expression related to diabetes by acting at the post-transcriptional level to control their target genes. They are involved in several crucial pathways associated with diabetes, including insulin secretion, cholesterol biosynthesis, fat metabolism, and adipogenesis [2]. miRNAs also play significant roles in pancreatic islet development, beta-cell dysfunction, insulin synthesis and secretion, and insulin resistance, which are key factors in the pathology of both Type 1 and Type 2 Diabetes Mellitus (T1DM and T2DM) [6]. Additionally, specific miRNAs have been implicated in the pathogenesis of diabetic complications, such as diabetic nephropathy, where miRNAs like miR-192, miR-216a, miR-217, and miR-377 are up-regulated [2]. These miRNAs can modulate the actions of growth factors and inflammatory factors, further influencing diabetic complications [5].", + "question": "How do microRNAs regulate gene expression related to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_1 new file mode 100644 index 0000000..d66558b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "f28836b7-0091-59ff-8d31-2ccad7341718", + "f7d5751d-c84d-5332-9dde-f31293ff02e3", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "301d6469-2a9c-5960-88ac-8437212d78ab" + ], + "id": [ + "chatcmpl-AIGl833nLoD9fbsUoJ9TogtCBZo31", + "c63cfaee-749e-547b-9c0a-086266f10670", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "91e1f097-b446-5915-9fcb-d38640d8a14a", + "488b9f81-e94f-56ad-9f28-dd71f3acd31f", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "40ecbff2-5039-57f0-a01e-9d412d559dbe" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome Browser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; Manhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics 1 Introduction GeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started in 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome Browser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; Manhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics 1 Introduction GeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started in 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + "answer": "GeneNetwork.org is a web service for systems genetics that started in 2001 as WebQTL, an online version of Ken Manly's Map Manager QT program [3]. It serves as a depository of datasets and tools for use in complex systems biology approaches to generate or predict higher-order gene function [8]. GeneNetwork.org is an open-access database that collates genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [9]. GeneNetwork.org helps scientists understand genetics by providing a platform for systems genetics, which involves the study of complex traits through the integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior across several species [10]. It offers tools for correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), making the study of complex traits widely available to the scientific community [2]. Additionally, it supports predictive medicine and systems genetics by constantly being maintained and improved with data from multiple species and multi-omics analysis [1].", + "question": "What is GeneNetwork.org, and how does it help scientists understand genetics?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_10 new file mode 100644 index 0000000..2023cc2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2019 - Implementation of Genomic Medicine.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf" + ], + "extraction_id": [ + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "2fd15885-4e19-536f-a90a-3650bd23c37e", + "406a0217-5585-5daf-88d0-5904cfb04c3b", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "90e220eb-61ba-56bd-b455-ac29a1df5867", + "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "28892088-5a95-56eb-822d-b12da3a612d0", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6" + ], + "document_id": [ + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "a7faf15a-ed90-575b-805c-11f33fb2d6dd", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "301d6469-2a9c-5960-88ac-8437212d78ab" + ], + "id": [ + "chatcmpl-AIGluZZhH7wm0mptVn5RRlhFxsJ3L", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "aaa9652c-64f3-53e7-ab24-48c5d19136d0", + "7dd2de60-d539-59e1-9c4c-2927367d650f", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a9bbd320-eb89-5ae7-a3af-703ca68c8305", + "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "7b626f27-5aed-5464-a20d-463954ff057d", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "40ecbff2-5039-57f0-a01e-9d412d559dbe" + ], + "contexts": [ + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and Translational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 GM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and Translational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 GM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "eron Genetics Center ( https://www.regeneron.com/ge - netics-center ), and aims to identify rare loss-of-function mutations in founder populations to delineate further the genetic factors that underpin health and disease. This ini - tiative is also addressed at developing countries and those in resource-limiting environments, under the coordina - tion of the Genomic Medicine Alliance ( http://www.ge - nomicmedicinealliance.org ), a founding partner of the", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + "answer": "GeneNetwork.org plays a significant role in personalized medicine by serving as an open-access, online data analysis resource for systems biology and systems genetics [1]. It is a tool for systems genetics and predictive medicine, which aims to predict and potentially avoid phenotypic outcomes such as diseases [2]. The platform supports the integration of networks of genes, transcripts, and traits, which is crucial for understanding complex genetic interactions and their implications for personalized medicine [10]. Additionally, GeneNetwork.org facilitates the comparison of data on drug responses with gene expression patterns, which is essential for tailoring therapeutic strategies to individual genetic profiles [9].", + "question": "What role does GeneNetwork.org play in personalized medicine?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_11 new file mode 100644 index 0000000..b0ea5b7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf" + ], + "extraction_id": [ + "362cb4d9-306b-5bbe-a2b7-3fedfb2cae2a", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "8dc4ac39-16e2-508e-aaa3-016c51410c79", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "40850ed1-db52-594e-a9d6-0b661e0bc494" + ], + "document_id": [ + "96657025-7e50-571d-9a6b-1a202cb8a690", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "3d629777-f1b6-5450-94ef-56736e5a4e10", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "c02542c0-eff8-5ec7-8f73-78f5d28d4226" + ], + "id": [ + "chatcmpl-AIGlzcVkzqXfNsnzBiVpVbwVplFu6", + "70bf64a4-327c-517d-8ccb-a9012bcfc453", + "c63cfaee-749e-547b-9c0a-086266f10670", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "b0020ac6-de30-5090-817a-d14d770abd1e", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "067136a5-b89e-5108-85b0-f638c041e68c", + "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc" + ], + "contexts": [ + "mation on gene function and how altered function leads to disease. Elucidating the mechanisms of action for newly minted disease genes is amajor bottleneck in translating genetic discoveries into new therapeutics.Addressing this limitation, it has been shown that networks can provideinsight on gene function [71,72] . The premise behind this is simple dgenes", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "vidual patients. For the time being, the contribu - tion of genetic information to therapy is most likely to come through the drug-discovery pipe - line. Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physi - ological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "biological function of the new gene list. As mentioned previously, GeneNetwork (www.genenetwork.org) is a collaborative Web-based resource equipped with tools and features for studying gene/gene and exploring genetic correlates to neurobehavioral phenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of gene expression and phenotypic data from a variety of species and brain regions, with a host", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed." + ], + "answer": "The information on GeneNetwork.org aids in developing new treatments for diseases in several ways: 1. **Insight into Gene Function**: GeneNetwork.org provides insights into gene function and how altered gene function can lead to disease. This understanding is crucial for translating genetic discoveries into new therapeutics, as it helps elucidate the mechanisms of action for newly identified disease genes, which is a major bottleneck in drug development [1]. 2. **Predictive Medicine and Systems Genetics**: The platform is an exciting resource for predictive medicine and systems genetics. It integrates data from multiple species and omics analyses, which can be used to predict phenotypic outcomes such as disease, potentially allowing for the development of treatments that can prevent these outcomes [2], [4]. 3. **Identification of Drug Targets**: Genetic information from GeneNetwork.org can be used to identify new targets for pharmaceutical intervention. This includes providing information about the long-term safety of pathway interventions, which is crucial for developing effective and safe treatments [5]. 4. **Exploratory and Statistical Analysis**: GeneNetwork.org is designed for exploratory and statistical analysis of large phenotype and genome datasets. This makes it practical to compare data on drug responses with gene expression patterns, facilitating the identification of potential therapeutic targets [8]. 5. **Studying Gene Networks**: By studying networks of genes, proteins, metabolites, and other biomarkers, GeneNetwork.org helps uncover disease genes. This network-based approach combines the effects of multiple genes, producing stronger signals and reducing the complexity of statistical analyses, which can accelerate the discovery of new treatments [10]. Overall, GeneNetwork.org serves as a comprehensive tool for researchers to explore genetic data and develop insights that are critical for the creation of new therapeutic strategies.", + "question": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_12 new file mode 100644 index 0000000..81e94df --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2009 - Visual analytics for relationships in scientific data (1).pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf" + ], + "extraction_id": [ + "1d401588-b6dc-532f-8194-4667a7d31153", + "1d401588-b6dc-532f-8194-4667a7d31153", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "697332a8-8630-50ff-aa2b-f33478931d24", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "a83ca198-3b9d-5355-aa82-30d89ebf018c", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "98aff04d-a5b2-5cca-bc1a-552055a74262" + ], + "document_id": [ + "17264155-b665-59db-94cb-f4d67eac20fc", + "17264155-b665-59db-94cb-f4d67eac20fc", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "c54da858-9620-588e-8e41-76a960af2ff6", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "d235d186-3d1c-5cde-90d5-9c140cd920f4" + ], + "id": [ + "chatcmpl-AIGm7DFsh1v2eeUURegyReODMaCec", + "509d3815-9994-5afc-9777-52eb80281dc8", + "9d6a0871-3235-5fd6-855a-897e6a177db4", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "e78c3922-952f-53ea-a1d5-8edd98f9b893", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "9c0d7bcf-242c-5ba7-86bb-df799e6e03a6", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "c63cfaee-749e-547b-9c0a-086266f10670", + "23de1e96-55b6-5062-a2e1-02bf06fd3565" + ], + "contexts": [ + "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play. Constructing gene networks can provide a convenient framework for exploring the context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological systems, the nodes in the network typically represent genes, gene products, or other", + "Genes do not carry out their functions in isolation of other genes, but instead oper- ate in complex networks that together, in a context-specic way, dene the complex behavior that emerges from biological systems. Therefore, understanding gene net- works in a diversity of contexts will lead to an increased understanding of complex system behavior, including disease. The reductionist approach to elucidating the complexity of biological systems", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "genotypes and phenotypes, geneticists hope to discover and interpret the network of causal genotype-phenotype relationships that determine a trait of interest. Systems genetics research often follows a workow of nding a gene network, nding regulators of that network, and then performing a focused ge ne perturbation experiment to determine the role of the associated network on gene expre ssion or function. To be- gin, a large gene correlation graph must be sifted through , to nd a highly connected", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "the risk of missing important biological phenomena [43]. 8.4 Defining gene and QTL networks In addition to the genetic dissection of phenotypic variation using QTL mapping techniques, systems geneticists are interested in r econstructing the biological net- works that connect genes, proteins and other traits based on their observed genetic (co-)variation. In this context, biological network s are often defined by graphical", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The strains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic correlations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15]. All data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16]. GN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be" + ], + "answer": "A gene network is a graphical model comprised of nodes and edges, where the nodes typically represent genes, gene products, or other biological entities [1]. These networks illustrate how genes do not function in isolation but operate in complex networks that define the behavior of biological systems [2]. Understanding gene networks is crucial for interpreting the roles of individual genes within the broader context of these networks, which can provide insights into complex system behaviors, including diseases [1], [2]. By considering genes within their networks, researchers can better understand the interrelationships and regulatory mechanisms that contribute to phenotypic traits and disease processes [4].", + "question": "What is a gene network, and why is it important for understanding genetics?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_13 new file mode 100644 index 0000000..b241543 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf" + ], + "extraction_id": [ + "6cbea84e-4d8d-5ce0-8e58-45ee75f6f908", + "2bdd2f18-e4d0-53e9-b0fa-a7ed8d710961", + "3033b643-e51e-5467-b7d7-6a5c27061cab", + "dbfd3de6-3641-5430-b694-682fed7b32e9", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "1047bf10-3878-5b70-8bb2-c0249f2a9c53", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "308bef07-d720-5686-990d-d1e26a48e8a1", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c" + ], + "document_id": [ + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "cc2690a9-5a87-5f09-87d5-115a6a6b8349", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f" + ], + "id": [ + "chatcmpl-AIGmBVU8OOwhBDyIls65dlks2MJDd", + "1762dc59-0e50-5b7e-bdc2-b754e0e57797", + "e030ce79-6970-5300-a1d8-1623d07c2157", + "48cb54db-68ef-50f0-bc7c-83b7db2ec9a5", + "bd9e8c5d-405c-5b8b-b731-bf4fdaea1b3a", + "01a09a4e-3c30-53b1-8819-6085d4886079", + "d261c68c-c253-52c9-8e27-f76fb8d0b4f8", + "21936758-94b1-506f-9229-77e26001ae44", + "c63cfaee-749e-547b-9c0a-086266f10670", + "94f60899-c281-586e-8741-135a4fef2663", + "d8162fdc-326a-5f90-9fa4-24d86d701184" + ], + "contexts": [ + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Another powerful feature of GeneNetwork is the ability to create and analyze whole collections of data. In Figure 3 there are boxes within the table that can be selected in order to form a trait collection. To do this, select the boxes in the table that su it the interests of the study, and press Add. This function allows groups of traits to be saved for later analysis such as the generation of a QTL, a network graph, and correlation matrix, some of which will be investigated further in", + "analysis in GeneNetwork, but there is an even more direct way to answer the same question. It is possible to query data sets in GeneNetwork from the Select and Search page using advanced options to locate the highest trait LRS values for any genomic interval, in this case the region within 2 Mb of Comt . (Note: You can explore this and other search options further by clicking the Advanced Search button and reading the section Advanced", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang et al, 2003) . GeneNetwork employs genotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on" + ], + "answer": "Researchers identify important genes for certain traits using GeneNetwork.org through a series of steps and tools provided by the platform: 1. **Data Selection and Trait Mining**: Researchers begin by selecting a data set and mining it for traits of interest based on user search queries [1]. This involves using the main search page to query specific data sets and identify traits that are relevant to their study. 2. **Trait Collection and Analysis**: Once traits are identified, they are selected and placed in a collection for further inspection and quantitative analysis [1]. This allows researchers to organize and focus on specific traits for deeper investigation. 3. **Advanced Search Options**: GeneNetwork offers advanced search options that enable researchers to query data sets for specific genomic intervals and locate traits with the highest likelihood ratio statistic (LRS) values, which are indicative of strong genetic associations [4]. 4. **Correlation and Genetic Linkage Mapping**: Researchers can establish associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions [5]. This helps in identifying candidate genes linked to specific traits. 5. **QTL Analysis and Network Graphs**: The platform allows for the generation of quantitative trait loci (QTL) analyses, network graphs, and correlation matrices, which are essential for understanding the genetic architecture of complex traits [3]. By utilizing these tools and processes, researchers can effectively identify and analyze genes that are important for specific traits using GeneNetwork.org.", + "question": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_14 new file mode 100644 index 0000000..f1352eb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2015 - An atlas of genetic correlations across human diseases.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2021 - Old data and friends improve with age Advancements with the updated tools.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf" + ], + "extraction_id": [ + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "70e38f86-69b7-515d-919e-b8d93f5c709f", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "46f604d3-ba70-5cca-8466-21381131697e", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce" + ], + "document_id": [ + "76a715a4-8222-598b-8e65-6d5b6e807989", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "7b1f602b-1534-5465-b026-03dedf01352d", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "55cb2c81-b699-54df-96ab-2bf0b888031e", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640" + ], + "id": [ + "chatcmpl-AIGmJRrNQ5y45QTYEPosOFommIdfp", + "21936758-94b1-506f-9229-77e26001ae44", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "38f4e070-1a03-566c-b261-c61ed61963c1", + "312eae52-ede7-5c13-8974-fce0126426cf", + "ed2def7c-a3bb-5d45-ae88-5100874b0837", + "01a09a4e-3c30-53b1-8819-6085d4886079", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "7beda13b-1ea5-53c0-9380-72eee2df79fe" + ], + "contexts": [ + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "201 5Nature America, Inc. All rights reserved. 6 ADVANCE ONLINE PUBLICATION Nature Ge Neticsa n a ly s i s 11. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565569 (2010). 12. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 7682 (2011). 13. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "medicine. GeneNetwork.org is a tool for quantitative genetics that started in 2001 as WebQTL [38]. It evolved from analyses of forward genetics in the BXD mouse family, to phenome-wide association studies and reverse genetics in a variety of species. Although GeneNetwork contains data for many species and populations, it most prominently contains data for the BXD family. Over 10,000 classical phenotypes, measured under a variety of environmental conditions, and", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the" + ], + "answer": "GeneNetwork.org can assist in understanding complex traits like height or intelligence through several key features: 1. **Analytical Tools and Data Sets**: GeneNetwork provides a variety of analytical tools that allow users to compare traits with numerous datasets available from other researchers. This includes microarray data of gene expression in the brain and other phenotypic data, which can be crucial for studying complex traits [1]. 2. **Systems Genetics Approach**: The platform offers a systems genetics approach, which helps illuminate the relationships between different biological system levels, such as the genome, transcriptome, and phenome. This comprehensive view can provide insights into the roles of individual genes and developmental pathways involved in complex traits [2]. 3. **Correlation and Genetic Linkage Mapping**: GeneNetwork allows for the establishment of associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions. This can help identify genetic factors contributing to complex traits like height or intelligence [6]. 4. **Data Mining and Trait Correlations**: The platform can be used to study correlations between traits and perform data mining in genomic regions containing candidates for quantitative trait genes. This feature is particularly useful for identifying genetic components of complex traits [4]. 5. **Multi-Omics Analysis**: GeneNetwork has been updated to include multi-omics analysis, which integrates various types of biological data. This holistic approach can enhance the understanding of complex traits by considering multiple layers of biological information [7]. Overall, GeneNetwork.org provides a comprehensive suite of tools and data that can facilitate the exploration and understanding of complex traits like height and intelligence through a systems genetics framework.", + "question": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_15 new file mode 100644 index 0000000..c79414e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "1993 - Genomic Damage and Its Repair.pdf", + "2007 - Trends in oxidative aging theories.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "2002 - Large genome rearrangements as a primary cause of aging.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2016 - Menopause Genome stability as new paradigm.pdf" + ], + "extraction_id": [ + "64063108-0ff2-54e5-9801-bc1c49cbdee4", + "752c6f1a-0c4d-5419-86cd-687d2aed7817", + "ead14808-bfb7-5e32-9830-28efaae71151", + "d620ea24-4422-5636-86f5-0943371a4a18", + "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "8f1a0875-8179-5d45-abc0-bbd4c9ac8da5", + "17b26647-4659-5f2d-a9b0-7c122d4b5d1a", + "72beba0d-8c77-5aa9-82ac-ddf6a19355ac", + "31088092-778f-59e0-a9de-5ec25c241aab", + "0855231d-cb95-540c-a3dd-c93729efb34c" + ], + "document_id": [ + "d049f302-a130-5ee4-a1b5-5091605d5173", + "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "8a8926dc-2360-5a54-b586-8acc34e51c32", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "564dead1-2737-572f-860c-f00de4d0395e" + ], + "id": [ + "chatcmpl-AIGmRJNSU1IpWwTrk2tDfmXqGWPRd", + "a9f7eda5-1b64-507e-95dd-07c81f2d603b", + "882149e3-8186-5577-a2a7-79f2659ff9b4", + "da4e59b7-d5b6-5992-9607-f6697c8f5276", + "4841d806-98b4-513e-94a2-714df6c896f5", + "fc10c968-3108-5c4b-a49c-cb0feabd18c5", + "eb8b89de-422a-5e9e-9ac8-60af4cd718c2", + "34e6b3c4-63bf-5198-ab09-2a7200a7c19a", + "beed04cc-28c7-5dc7-b334-51226a217439", + "badf3a36-1f99-58aa-b80c-725eccf4e8f3", + "c35d1f43-c3bd-5cac-ae4d-937be35f1121" + ], + "contexts": [ + "logical phenomena is often facilitated by the study of genetic mutants, and, in the case of humans, genetic disorders. Accordingly, a search was made, over the years, for genetic disorders characterized by premature aging. If DNA dam- age and repair has anything to do with aging it should be evidenced in such individuals. Martin (1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. About 21 feahares are considered as markers for", + "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA 45:3545; 1959. [316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of aging. Mech. Ageing Dev. 123:907915; 2002. [317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res. 447:117135; 2000. [318] Martin, G. M. Genetic syndromes in Man with potential relevance to the pathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "19 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. 2017;94:3440. 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. DNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777. 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation accumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria Syndrome: A premature aging disease caused by LMNA gene mutations. Ageing Res. Rev. 33, 1829. 144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and related RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. 145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? Ageing Res. Rev. 33, 3651. 146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542. [2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and Rose, M.R. (1996) Metabolic aspects of the trade-off between fecundity and longevity in Drosophila melanogaster ,Physiol. Zool. 69, 11751195. [3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA mutations and deletions were the driving force behind the observed accelerated aging phenotypes(18). THE LINK BETWEEN NUCLEAR GENOME INTEGRITY AND PREMATURE AGING The notion that the majority of currently identied progeria syndromes originate from defects in genome maintenance highlights the importance of the condition of DNA in the process of", + "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based premature aging,NatMed. 2005;11:780 785. 13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when repairgetsold,NatMed. 2005;11:718 719. 14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De SandreGiovannoli A,LvyN.HGPSandrelatedpremature aging disorders: Fromgenomicidentification tothefirsttherapeutic approaches, MechAgeingDev.2008;129:449 459. 15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "Nature Genetics | Volume 55 | February 2023 | 268279 278 Article https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Hum. Genet. 105, 237257 (2019). 22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry 50, 23212329 (2011).", + "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al., Premature aging in mice decient in DNA repair and transcription, Science 296 (2002) 12761279. [37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A. Reijo Pera, Genetic markers of ovarian follicle number and menopause in women of multiple ethnicities, Hum. Genet. 131 (2012) 17091724." + ], + "answer": "Yes, there are known genetic mutations that cause premature aging. Some specific genetic syndromes associated with premature aging include: 1. Hutchinson-Gilford Progeria Syndrome, which is caused by mutations in the LMNA gene [4]. 2. Rothmund-Thomson syndrome and related disorders, which are associated with mutations in the RECQL4 gene [4]. 3. Ataxia-telangiectasia, which is another genetic disorder linked to premature aging [4]. Additionally, Martin (1978) listed 162 genetic syndromes in humans that exhibit some or many signs of premature aging [1]. These conditions highlight the connection between genetic mutations and premature aging.", + "question": "Are there any known genetic mutations that cause premature aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_16 new file mode 100644 index 0000000..cfa8250 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2015 - Gene network of a phosphoglycerate mutase in muscle wasting in mice.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2008 - Systems_genetics_can_provide_new_insight.pdf", + "2017 - Systems genetics of liver fibrosis.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "b71c7c43-8bd9-5d7e-b337-a8c0396f96b7", + "833f822f-9080-5434-b9a1-2473531becfe", + "bfd98d6d-b026-5bbd-896f-b70045a032c8", + "1c595cd2-cdf9-58c7-a344-902eb2facf31" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "73724c99-98df-53b2-a378-29c8b4faa171", + "912c42f2-1802-5fe9-b9dd-871c5290dd18", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "0bc5b1ee-f7fe-5d12-9ebf-ab7363119424", + "4efaa694-a204-54e3-8f4b-caa322574f0a" + ], + "id": [ + "chatcmpl-AIGmWH13RNXnSJZf14g9zwI9AF1X4", + "c63cfaee-749e-547b-9c0a-086266f10670", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "a0c173a5-8685-50df-8110-8d7ec02cdbf3", + "27eda296-b0b7-5ae4-881e-31987ba63dec", + "55461d7c-995b-5f5a-be38-a64acd4a904a", + "fb6033cf-3df1-5dd6-b3ed-1d32913800b2" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "resources, gene expression pro les, and gene network constructions, methods for the analysis of gene function have been revolutionised in the past few years. One great resource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for systems genetics (Andreux et al., 2012). It has been designed for multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-", + "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems genetics and systems behavioral approaches. Web services such as GeneNetwork and its companionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID (Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 ) can now be used as virtual and free laboratories to test specific biological hypothesis, or they can be used to generate new ideas ab initio .", + "Its use is centred upon user-specied genes and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of the GeneNet- work and systems genetics further in a number of areas. In partic- ular, increasing the number of strains studied can increase the mapping resolution. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-", + "gration enhances the chance to detect genuine modi ers across organs. GeneNetwork is a valuable platform that can be used by researchers without advanced skills of bioinformatics to perform systems genetics analyses. The next step would be to establish soft- ware tools that allow researchers to combine datasets from multiple resources and mapping analyses in different crosses and species (e.g. intercross, recombinant inbred lines, and human data). References" + ], + "answer": "The research done through GeneNetwork.org has several practical applications: 1. **Predictive Medicine and Systems Genetics**: GeneNetwork is a valuable resource for predictive medicine and systems genetics, providing tools and data for multi-omics analysis across multiple species [1]. 2. **Teaching Tool**: It serves as a teaching tool in neuroscience and genetics, allowing educators to use it for dry-lab teaching and helping students explore gene-to-phenotype relationships [2]. 3. **Exploration of Systems Genetics Data**: GeneNetwork is used to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [4]. 4. **Complex Systems Biology Approaches**: It provides datasets and tools for complex systems biology approaches, aiding in the generation or prediction of higher-order gene functions [5]. 5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork can be used as a virtual laboratory to test specific biological hypotheses or to generate new ideas from scratch [8]. 6. **Identification of Regulatory Genes**: The platform can identify novel potential master regulatory genes for further investigation, enhancing the understanding of genetic regulation [9]. 7. **User-Friendly Systems Genetics Analyses**: It allows researchers without advanced bioinformatics skills to perform systems genetics analyses, making it accessible to a broader range of scientists [10].", + "question": "What are the practical applications of the research done through GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_17 new file mode 100644 index 0000000..74708a3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "d1c32c32-42c8-5065-b7f2-bd2a0baeae62", + "2bdd2f18-e4d0-53e9-b0fa-a7ed8d710961", + "6cbea84e-4d8d-5ce0-8e58-45ee75f6f908", + "779b4029-6cc7-535e-a8b7-0ee31fa97162", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "dc001755-2e77-5b41-8617-263b3ba35af8", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "83ae495f-31a2-5977-a63a-57e704c394e2", + "0e3a5e40-06b0-58d4-b495-3093954ed17b" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "73724c99-98df-53b2-a378-29c8b4faa171", + "08b12d72-9776-5acb-b1ef-7ee402781897", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448" + ], + "id": [ + "chatcmpl-AIGmdOlKLAeARCOPtbkwth6fOr9HL", + "c63cfaee-749e-547b-9c0a-086266f10670", + "bd2eb0ef-24a1-55ff-8597-c21dff0ecf0a", + "e030ce79-6970-5300-a1d8-1623d07c2157", + "1762dc59-0e50-5b7e-bdc2-b754e0e57797", + "f574ef17-062c-5bc8-be3e-81184e141970", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "251de62d-6e8e-50c7-9616-7fea05a250fb", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "db6bfa4e-9612-5f7e-8b7f-162f60b91c9d", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table , and throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID= sharinginfo&GN_AccessionId=600 . GeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/ genenetwork/genenetwork1 .", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "Fig. 2. GeneNetwork main search page and organization. Most analyses in GeneNetwork will follow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) and mined for traits of interest based on user search queries ( B). Traits are then selected from the search ( C) and placed in a collection for further inspection and quantitative analysis (D). The banner menu contains additional search options and helpful resources under the", + "1. Data Once you have navigated to genenetwork.org, t here are two ways to search for data in GN. The first is to use the global search bar located at the top of the page (Figure 1 ). This is a new feature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the datasets. This will give the user data for that search term across many different species, groups, and types of data. Because of this, the global search bar is a good area to start ones searches if", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "abundance data sets directly within GeneNetwork's ana- lytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (bar- ley) in a database that has been designed for an animal model species (mouse) with well established genome", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression" + ], + "answer": "To access and use the data available on GeneNetwork.org, you can follow these steps: 1. **Navigating to the Website**: Start by visiting the GeneNetwork website at www.genenetwork.org [8]. 2. **Searching for Data**: There are two primary ways to search for data on GeneNetwork: - Use the global search bar located at the top of the page. This feature allows you to search for genes, mRNAs, or proteins across all datasets, providing data across various species, groups, and types of data [5]. - Alternatively, you can follow the main search workflow, which involves selecting a dataset, mining it for traits of interest based on user search queries, selecting traits from the search, and placing them in a collection for further inspection and quantitative analysis [3]. 3. **Analyzing Data**: Once you have selected the data, GeneNetwork provides an analytical environment where you can perform correlation analysis and linkage mapping. This environment helps identify and substantiate gene targets for further research [7]. 4. **Accessing Genotype Files**: If you need genotype files, they can be accessed directly via a specific URL: http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=600 [2]. 5. **Using Additional Resources**: The banner menu on the main search page contains additional search options and helpful resources to assist with your analyses [3]. By following these steps, you can effectively access and utilize the data available on GeneNetwork.org for your research needs.", + "question": "How can I access and use the data available on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_18 new file mode 100644 index 0000000..6db16f1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2015 - Gene network of a phosphoglycerate mutase in muscle wasting in mice.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "0c76461f-34ff-5604-aa4c-12eb9d2877aa", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "28892088-5a95-56eb-822d-b12da3a612d0", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "b71c7c43-8bd9-5d7e-b337-a8c0396f96b7" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "73724c99-98df-53b2-a378-29c8b4faa171", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "912c42f2-1802-5fe9-b9dd-871c5290dd18" + ], + "id": [ + "chatcmpl-AIGmkanSOkp2jhcX3KUkfSSqCSJEs", + "c63cfaee-749e-547b-9c0a-086266f10670", + "105b8e0a-a88c-59a6-9d39-7d894d059b73", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "312eae52-ede7-5c13-8974-fce0126426cf", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "7b626f27-5aed-5464-a20d-463954ff057d", + "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "a0c173a5-8685-50df-8110-8d7ec02cdbf3" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "18 GeneNetwork Time Machine : Full versions from 2009 to 2016 (mm9); UTHSC Genome Browser Classic and Newest ; UTHSC Galaxy Servic e; UTHSC Bayesian Network Web Server ; GeneNetwork Classic on Amazon Cloud ; GeneNetwork Classic Code on GitHub ; GeneNetwork 2.0 Development Code on GitHub ; and GeneNetwork 2.0 Development. Technologies or techniques: None Inventions, patent applications, and/or licenses: None Other products: None", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 ).", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", + "resources, gene expression pro les, and gene network constructions, methods for the analysis of gene function have been revolutionised in the past few years. One great resource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for systems genetics (Andreux et al., 2012). It has been designed for multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-" + ], + "answer": "The provided context does not explicitly mention any specific recent discoveries made using GeneNetwork.org. However, it highlights the platform's capabilities and improvements, such as the addition of data from 10 species, multi-omics analysis, updated code, and new tools, which make it a valuable resource for predictive medicine and systems genetics [1]. Additionally, GeneNetwork.org is described as a powerful statistical platform for online network analyses and mapping, which could enable numerous breakthroughs in neuroscience and other fields [7]. While these improvements and capabilities suggest potential for discoveries, specific recent discoveries are not detailed in the context provided.", + "question": "What are some recent discoveries made using GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_19 new file mode 100644 index 0000000..60d520c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2014 - Sirt1 induction confers resistance to etoposide-induced genotoxic apoptosis in thyroid cancers.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GN.pdf", + "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.pdf", + "2022 -Chunduri- Drugs Animal Models.pdf", + "2019 - A multi-omics digital research object for the genetics of sleep regulation.pdf", + "2016 - Systems Genetics of Obesity.pdf", + "2017 - Systems genetics of obesity.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "e3d1b792-6241-5ba3-b06f-ee29eb0106fc", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "50d920fa-3482-52ca-899f-15b182fdb4fd", + "ee874620-8c4e-55df-8274-2dcd4eba2ca9", + "4cafc4e9-69df-5a08-921c-de6c66267056", + "a002e2e0-b978-540d-b435-5701c30496b6", + "d214b44c-c033-59f7-b120-fa4d6bf35bb4", + "674a8666-6310-5df3-8539-e274cd629e9c", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "18e62e2f-643c-5c42-b80a-bab5432a8894", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "6f5d0c5b-0bbb-5eca-9e3e-73c3b0675472", + "d71efa0d-5de8-549c-964d-489ef6b73a1f", + "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45", + "af97f766-ca4d-56c0-9eb8-ba6c5e7db1da", + "c38d1bad-8690-5d4d-a60a-dcbb4ac4aa93", + "f10cf311-0397-5c0a-81e0-3b84090e434b", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104" + ], + "id": [ + "chatcmpl-AIGmr7v0rrhLH7kaV38yDCwjdEEpc", + "c63cfaee-749e-547b-9c0a-086266f10670", + "a2875189-1592-59ad-ad10-f3c4911411e2", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "8f734e2a-cd29-5021-84be-a9e08bc21a99", + "219cfeab-8877-5c92-92d0-87b17c0d4206", + "8a3abc37-292a-5bd3-9527-bcf17dc9eafc", + "29c406c6-34e1-5f8a-8a6f-1b239dd633ae", + "45ce962b-f534-59a7-ab21-c5f858d4ec20", + "19ba23ee-9d24-55cc-85cb-bee95894f710", + "4188099c-aba1-5f0d-b2ec-a7c8f5bb1bc5" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "files), and GeneNetwork (a free scientific web resource, http://www.genenetwork.org/). Statistical analysis was performed using GraphPad Prism (GraphPad Software, Inc., CA, USA).", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and informatics support. Conicts of Interest: The authors declare no conict of interest. References 1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018. [CrossRef]", + "9 Scientific Data | (2019) 6:258 | https://doi.org/10.1038/s41597-019-0171-x www.nature.com/scientificdata www.nature.com/scientificdata/with more than 10% missing information, low quality ( <5000), and redundant information were removed. GeneNetwork genotypes, which were discrepant with our RNA-seq experiment, were tagged as unknown (mean of 1% of the GeneNetwork genotypes/strain [0.05% n 8%]). Finally, GeneNetwork and our RNA-seq", + "1. Phenotypic data should be quality checked and preprocessed before being uploaded to GeneNetwork. This includes nor- malization of data, removal of outliers or windsorization, even- tually transformation of data to obtain normal distribution. 2. When uploading data to GeneNetwork for permanent and public storage, make sure to follow the GeneNetwork naming guide for phenotypes. 3. When uploading your own data make sure that for any pheno-", + "1. Phenotypic data should be quality checked and preprocessed before being uploaded to GeneNetwork. This includes nor- malization of data, removal of outliers or windsorization, even- tually transformation of data to obtain normal distribution. 2. When uploading data to GeneNetwork for permanent and public storage, make sure to follow the GeneNetwork naming guide for phenotypes. 3. When uploading your own data make sure that for any pheno-", + "analysis of behavior and for neurologic diseases are provided in the study by Mulligan et al. (2017) . GeneNetwork.org is committed to data and code workflows that are FAIR compliant, ensuring that those who generate data and key ideas get the deserved credit. To further ensure effective and secure dissemination of data and ideas, as well as improved reproducibility, the GeneNetwork.org infrastructure is currently being redesigned using more modular structures and APIs that" + ], + "answer": "Scientists ensure the accuracy of the data on GeneNetwork.org through several methods: 1. **Quality Control and Normalization**: Data are entered into GeneNetwork after being processed through systems like PhenoGen, which have extensive capabilities for normalization and quality control [3]. 2. **Quality Checking and Preprocessing**: Phenotypic data undergo quality checks and preprocessing before being uploaded to GeneNetwork. This includes normalization, removal of outliers, and transformation of data to achieve a normal distribution [8]. 3. **Data Curation and Informatics Support**: The GeneNetwork.org team provides excellent data curation and informatics support to maintain data accuracy [4]. These steps help ensure that the data on GeneNetwork.org is accurate and reliable for scientific research.", + "question": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_2 new file mode 100644 index 0000000..9510d12 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "82e07232-dd92-52f6-8230-d90a03c71b4f", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "e2190b29-6d30-58fb-978f-d052582698bd", + "40850ed1-db52-594e-a9d6-0b661e0bc494", + "a8b40857-7ae8-512a-9817-bea1ae3345ba" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "96657025-7e50-571d-9a6b-1a202cb8a690", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "ac61753e-bcb2-55c3-804b-e821e3d1a4ad" + ], + "id": [ + "chatcmpl-AIGlD8JegvZvagzZ7ZZc2o1BsPAjA", + "c63cfaee-749e-547b-9c0a-086266f10670", + "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "1c8d31d6-bd59-56da-83b8-f603b4a9ec2b", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "d348dfa8-c6c5-5514-8c64-920f254b9571", + "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "8b8a24da-a175-5cb8-91bd-8966fca5d344" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "Based on this, Goh et al. created networks using data from the Online Mendelian Inheritance in Man (OMIM) [18]database that houses lists of disease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in the same gene, and the disease gene network where gene nodes were", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "atic way. Users begin by selecting one or more human diseases and clicking on Compare. The genes associated with the selected disease are tested for enrichment against all sets of known associat ed genes for worm phenotypes. The result reveals functionally coherent , evolution- arily conserved gene networks. Alternatively, users can also start by selecting worm pheno types, which are tested against human diseases. In addition to cross -species", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "subnetworks GeneNetwork (www.genenetwork.org) is a depository of data- sets and tools for use in complex systems biology approaches in order to generate or predict higher order gene function ( 23, 24 )." + ], + "answer": "Researchers use GeneNetwork.org to study diseases by leveraging its capabilities as a bioinformatics tool for systems genetics analysis. This platform allows researchers to explore large phenotype and genome datasets from multiple species, which are essential for understanding complex biological networks and predicting molecular interactions [4], [5]. GeneNetwork.org supports a systems genetics approach, which examines how diverse sets of genetic and molecular markers contribute to phenotypes and diseases, rather than focusing on single gene mutations [2]. This approach is facilitated by the extensive data available on the platform, including gene expression patterns and drug response data, which can be compared and analyzed statistically [4]. The platform also enables correlation and network analysis, allowing researchers to compare associations between tissues and across different species, such as rodents and humans [6]. By studying networks of genes, proteins, metabolites, and other biomarkers, researchers can model genuine biological pathways, which helps in uncovering disease genes and understanding complex diseases [9]. Overall, GeneNetwork.org provides a comprehensive resource for predictive medicine and systems genetics, aiding researchers in constructing biological networks that are predictive of disease outcomes [1], [5].", + "question": "How do researchers use GeneNetwork.org to study diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_20 new file mode 100644 index 0000000..4ad1e6d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "2007 - Identifying genomic regulators of set-wise co-expression.pdf", + "2007 - Systems genetics the next generation.pdf", + "2008 - Dynamic Visualization of Coexpression in Systems Genetics Data.pdf", + "2005 -Lovinger- Lab models of alcoholism.pdf", + "2005 - Laboratory models of alcoholism treatment target identification and insight into mechanisms.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf" + ], + "extraction_id": [ + "1d401588-b6dc-532f-8194-4667a7d31153", + "40850ed1-db52-594e-a9d6-0b661e0bc494", + "51cb3178-b604-5869-98bd-cd32def3bd54", + "eff279b1-0d36-5dd4-9230-72adfe2ed79a", + "84072d11-c436-5405-a356-7dd9886db6e8", + "ee39bb1c-a55c-5aad-8e43-77eb8f38ff85", + "440c9c21-a03a-576f-8206-2a354508bb82", + "6ee23564-711a-5bc9-bb04-14a4b611c8bf", + "fad81c4c-0da6-54c0-898a-755ffb8870b6", + "223e442e-898d-5aea-866a-5cdc0ac915e8" + ], + "document_id": [ + "17264155-b665-59db-94cb-f4d67eac20fc", + "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "34b8aa80-7150-5c53-8cef-9f1d614ae886", + "a6202d00-514d-5b48-89cd-5fc9649c0ee4", + "ff6bf912-b590-582e-a841-6499cea56508", + "91621f34-9602-5cdc-91d8-c608c4e0b02c", + "148a4120-6ab8-554a-ab30-3394f61f98e1", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "17264155-b665-59db-94cb-f4d67eac20fc", + "ef974b09-4ea2-5382-85e5-c2169f440fda" + ], + "id": [ + "chatcmpl-AIGmxl54ZXUf76PPUFFgbX3aFiiFS", + "509d3815-9994-5afc-9777-52eb80281dc8", + "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "85a8d5cf-0da0-5273-b1f2-f10e440ea24e", + "fc76d75c-37d3-5a29-9093-d25ff746b465", + "a06d0485-b264-53b2-8a13-0c13c600c026", + "8358a79b-ac66-510f-9ee2-9763a0f9d95e", + "343c3cb8-779b-571c-9633-cbd37a941f25", + "1b8201cc-bdc4-5cb7-ad3b-da9d6ae59fc8", + "c6c3f636-c6bb-539d-861f-6cf1145f4f50", + "72cac585-5de7-56ca-8ea5-c133d3ff7acf" + ], + "contexts": [ + "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play. Constructing gene networks can provide a convenient framework for exploring the context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological systems, the nodes in the network typically represent genes, gene products, or other", + "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "traditional genetical genomics approaches. It should also be noted that our approach is different from studying gene-gene regulation within a pathway, which focuses on the interactive activities of individual gene pairs genes within a pathway. A biological pathway is defined as a series of molecular interactions and reactions. If there are subtle changes in the expression level of a few genes located in the upper cascade of a", + "genes rapidly that may be in the same genetic network as the gene you are interested in. Then you need to validate the role of that gene and to identify its function in that network. The point is this is a powerful methodology that can provide data in half an hour that allows you to form hypotheses that you can then spend years investigating. Reference Lee PD, Ge B, Greenwood CM et al 2006 Mapping cis-acting regulatory variation in recombi- nant congenic strains. Physiol Genomics 25:294302", + "ment to determine the role of the associated network ongene expression or function. To begin, a large genecorrelation graph must be sifted through, to find a highlyconnected subgraph that corresponds biologically to a genenetwork in which genes are expressed together, presumablyto regulate or subserve a common function. They must thenfind a small set of causative genes, highly correlated withthe subgraph and likely to regulate coexpression, to be usedas targets of focused investigation. By manipulating the", + "Confronted with this daunting complexity, the field often progresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "Confronted with this daunting complexity, the field often progresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "From the network, modules of coexpressed genes can be obtained, i.e. com- munities of highly interconnected nodes within the graph. Such coexpressed modules can then be studied as putative functional units, thereby considerably reducing the dimensionality of the data. Different approaches have been proposed, many of which are inspired by social network resear ch. Chesler et al. choose to focus on sets of genes in which all nodes are inter connected; such sets are termed", + "large-scale human and experimental populations, focusing on how a single protein or RNA impacts disease will ultimately give way to how a network of gene interac- tions impacts disease. The integration of genetic, molecular proling, and clinical data has the potential to paint a more detailed picture of the particular network statesthat drive disease, and this in turn has the potential to lead to more progressive treat- ments of disease that may ultimately invol ve targeting of whole networks as opposed", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns" + ], + "answer": "The difference between looking at one gene and studying a whole gene network lies in the scope and context of the analysis. When examining a single gene, the focus is on understanding the role and function of that specific gene, often in isolation. This approach can be limited because it does not consider the interactions and relationships that gene may have with others. In contrast, studying a whole gene network involves analyzing a system of interconnected genes, which provides a broader context. A gene network is a graphical model where nodes represent genes or gene products, and edges represent interactions between them [1]. This approach allows researchers to explore how multiple genes interact within biological pathways, potentially uncovering complex relationships and combined effects that a single-gene analysis might miss [2]. By studying gene networks, researchers can gain insights into the collective behavior of genes, which can be crucial for understanding complex diseases and biological functions [2]. Additionally, gene networks can help identify highly connected subgraphs that correspond to biologically relevant networks, aiding in the identification of causative genes and their regulatory roles [5].", + "question": "What\u2019s the difference between looking at one gene and studying a whole gene network?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_21 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_21 new file mode 100644 index 0000000..1b36f73 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_21 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2008 - Systems_genetics_can_provide_new_insight.pdf", + "2007 - Systems genetics the next generation.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "2009 - Herding cats the sociology of data integration.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "bfd98d6d-b026-5bbd-896f-b70045a032c8", + "d7d6d0a8-db35-5be6-a3fc-82f347e5d37b", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "833f822f-9080-5434-b9a1-2473531becfe", + "b3c1f52a-f5be-586f-a3c9-04bed83fb12e", + "83ae495f-31a2-5977-a63a-57e704c394e2", + "c5e5623c-2373-535c-978d-3af1cec77f1a" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "0bc5b1ee-f7fe-5d12-9ebf-ab7363119424", + "a6202d00-514d-5b48-89cd-5fc9649c0ee4", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "08b12d72-9776-5acb-b1ef-7ee402781897", + "15e14cfc-dbeb-5998-b42a-9fbfb7464bf2" + ], + "id": [ + "chatcmpl-AIGn3AbKULjVBzRjcrJYXxi0av0dH", + "c63cfaee-749e-547b-9c0a-086266f10670", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "55461d7c-995b-5f5a-be38-a64acd4a904a", + "f3821133-e965-535b-88d0-f43b14d311b6", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "27eda296-b0b7-5ae4-881e-31987ba63dec", + "b82c0ee6-9e1b-595d-95b3-1cc9d7aff44d", + "db6bfa4e-9612-5f7e-8b7f-162f60b91c9d", + "dc06eebe-4d67-5bd0-9ed8-3dd9d5a588bd" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "Its use is centred upon user-specied genes and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of the GeneNet- work and systems genetics further in a number of areas. In partic- ular, increasing the number of strains studied can increase the mapping resolution. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-", + "and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of GeneNetwork and systems genetics in a number of areas. In particular, the mapping resolution can be increased by increasing the number of strains studied. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-morphisms increases dramatically. In this context, the availability of 1000 RI strains from The Gene", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems genetics and systems behavioral approaches. Web services such as GeneNetwork and its companionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID (Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 ) can now be used as virtual and free laboratories to test specific biological hypothesis, or they can be used to generate new ideas ab initio .", + "within GeneNetwork's analytical en vironment we provide simple we b access to the data for the research community. In this envi ronment, a combination of correl ation analysis and linkage mapping provides the potential to identify and substant iate gene targets for saturation mapping and positional cloning. By integratin g datasets from an unsequenced cr op plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome", + "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "will be convincing both the community and its leaders that it is doable today. acKnoWledgmenTs Thanks to Glenn D. Rosen and Kathryn Graehl for comments and editorial improvements. Thanks also to the NIH for support of many data sets for GRPs now being incorporated into GeneNetwork, a pilot project in systems genetics. references Carneiro, A. M., Airey, D. C., Thompson, B., Zhu, C. B., Lu, L., Chesler, E. J., Erikson, K. M., and Blakely, R. D. (2009). Functional coding variation" + ], + "answer": "GeneNetwork.org can contribute to advancements in genetic engineering in several ways: 1. **Predictive Medicine and Systems Genetics**: GeneNetwork.org is an exciting resource for predictive medicine and systems genetics, providing a platform for multi-omics analysis and data from multiple species [1]. This can help in understanding complex genetic interactions and predicting phenotypic outcomes, which are crucial for genetic engineering. 2. **Identification of Master Regulatory Genes**: The platform allows users to identify novel potential master regulatory genes for further investigation [4]. This capability is essential for genetic engineering as it can help in pinpointing key genes that control important traits or pathways. 3. **Increased Mapping Resolution**: By increasing the number of strains studied and the genetic diversity of the founders of a recombinant inbred (RI) set, GeneNetwork.org enhances the mapping resolution [5]. This improvement can lead to more precise genetic modifications and better understanding of genetic variations. 4. **Construction of Biological Networks**: GeneNetwork.org facilitates the construction of biological networks by integrating data on DNA variation, gene expression, protein interactions, and DNA-protein binding [6]. These networks are predictive of molecular interactions, which can guide genetic engineering efforts. 5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork.org, along with its companion tools, can be used as a virtual and free laboratory to test specific biological hypotheses or generate new ideas [7]. This feature supports experimental design and innovation in genetic engineering. Overall, GeneNetwork.org provides a comprehensive analytical environment and a wealth of data that can significantly aid in the advancement of genetic engineering by enabling detailed genetic analysis, hypothesis testing, and the discovery of key genetic regulators.", + "question": "How can GeneNetwork.org contribute to advancements in genetic engineering?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_3 new file mode 100644 index 0000000..6576029 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2019 - Different genetic mechanisms mediate spontaneous versus UVR-induced malignant melanoma.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf" + ], + "extraction_id": [ + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "9383f177-92a5-5264-9d81-ff623d0614e3", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "4eb8a5f8-5936-523d-971d-302348d6d62f", + "223e442e-898d-5aea-866a-5cdc0ac915e8", + "a771f252-00da-5f52-9c29-d006313c9e7b", + "3e0c2a06-e6de-5888-a360-a2c483d9f744" + ], + "document_id": [ + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "db0aa4b3-66ec-5d51-be72-2a1289db944a", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "8161c536-c996-5660-b6ae-2d33c5d4aa9a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861" + ], + "id": [ + "chatcmpl-AIGlKS6puXfNaWUwFF42aUVBShhJ7", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "f1d40272-4a35-5b52-b3a8-3a0e7e8626d2", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "c63cfaee-749e-547b-9c0a-086266f10670", + "01a09a4e-3c30-53b1-8819-6085d4886079", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "a11bd1db-1c26-54fa-85c8-39bb745d2ebf", + "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "c0150694-7ee6-5e4f-a880-302cfd596718", + "312eae52-ede7-5c13-8974-fce0126426cf" + ], + "contexts": [ + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "Molecular Genetics and Genomics 1 3 as overexpression, knockdown, knockout and mutation (Online Resource 1). Gene network construction Genegene interaction data were extracted from the STRING database (http://strin g-db.org/) (Christian etal. 2003), a web resource that includes comprehensively predicted and known interaction information. Then, the genegene interaction pairs were imported into Cytoscape software (Version 3.5.1) (http://cytos cape.org/ ) (Smoot etal. 2011 ) to construct a", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "is shown in Figure 1A. Associations between transcript abundance, phenotypic traits and genotype can be estab- lished either using correlation or genetic linkage mapping functions [29,30]. The main page of GeneNetwork at http://www.genenetwork.org provides access to subsets of data through pull-down menus that allow specific data sets to be queried. The datasets can be further restricted using a single text box for specific database entries to query probe set or trait ID, or annotations associated with", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "occurrence; GN, gene neighbor; GT, genetic interaction; LC, literature-curated protein interactions; MS, affinity purification/mass spectrome try; PG, phy- logenetic profiles; PI, fly protein interactions; TS, tertiary structure; and YH, yeast two-hybrid). Detailed descriptions are listed in Suppleme ntal Table S1. ( B) Essential genes were highly interconnected in HumanNet, and thus predictable from the network, as shown by ROC analysis. Genes were ranked by their sum", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns", + "(http://string-db.org/ ). STRING creates networks representing the best available knowledge of gene interconnections. Each protein-protein interaction is annotated with scores indicating how likely an interaction should be true. Scores rank from 0 to 1, with one being the highest confidence. A score of 0.5 indicates roughly every second interaction might be erroneous. Gene-gene co-expression cor- relations were computed as Pearson product-moment correlations (r) in Genenetwork.org after removing outliers.", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published" + ], + "answer": "GeneNetwork.org provides several tools and resources to explore how genes interact with each other. It is a bioinformatics tool that allows users to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [1]. GeneNetwork offers correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [3]. Additionally, it provides access to a wide variety of data, including genotypes and phenotypes, which can illuminate relationships between different levels of a biological system, such as the genome, transcriptome, and phenome [6]. GeneNetwork also computes gene-gene co-expression correlations, which are essential for understanding gene interactions [9].", + "question": "What can GeneNetwork.org tell us about how genes interact with each other?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_4 new file mode 100644 index 0000000..b20b8a1 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "2019 - Implementation of Genomic Medicine.pdf", + "2012 - Generating Embryonic Stem Cells from the Inbred Mouse Strain DBA2J, a Model of Glaucoma and Other Complex Diseases.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf" + ], + "extraction_id": [ + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "82e07232-dd92-52f6-8230-d90a03c71b4f", + "a58546e6-fe89-5d04-8adb-08d1991dc53c", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "2455cf6d-4c9b-5272-8650-da127cc329e8", + "90e220eb-61ba-56bd-b455-ac29a1df5867", + "ee03f7c5-6eee-5c66-8174-688f06da1587", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a" + ], + "document_id": [ + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "96657025-7e50-571d-9a6b-1a202cb8a690", + "c37e2ace-171b-5776-8969-86eda9736481", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "67e804db-8127-5938-8d7f-a5918cdf4f86", + "a7faf15a-ed90-575b-805c-11f33fb2d6dd", + "a9b08d55-2f85-5d3a-abbf-389eed34009c", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104" + ], + "id": [ + "chatcmpl-AIGlO1Tf6FzOyoXrb1Vnt5VYQUM0R", + "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "1c8d31d6-bd59-56da-83b8-f603b4a9ec2b", + "f8a32960-cfe3-5440-9d5c-b55dfe52ea6d", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "d8162fdc-326a-5f90-9fa4-24d86d701184", + "c63cfaee-749e-547b-9c0a-086266f10670", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "a9bbd320-eb89-5ae7-a3af-703ca68c8305", + "504b72fb-9a5e-53a4-b6a6-0fc6be18ec4e", + "a1124460-ae34-57fb-846b-e033f4bbf49c" + ], + "contexts": [ + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "Based on this, Goh et al. created networks using data from the Online Mendelian Inheritance in Man (OMIM) [18]database that houses lists of disease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in the same gene, and the disease gene network where gene nodes were", + "Genetics Home Reference - Genetics Home Reference provides consumer-friendly information about the effects of genetic variations on human health. http://ghr.nlm.nih.gov/ Gene Reviews Features expert-authored, peer-reviewed, current disease descriptions that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "of links to external resources for tracing the interrelationships of a gene among multiple Web-based resources. GeneNetwork also offers a number of correlation and mapping strategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to make the study of complex traits through the use of systems genetics widely available to the scientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained", + "eron Genetics Center ( https://www.regeneron.com/ge - netics-center ), and aims to identify rare loss-of-function mutations in founder populations to delineate further the genetic factors that underpin health and disease. This ini - tiative is also addressed at developing countries and those in resource-limiting environments, under the coordina - tion of the Genomic Medicine Alliance ( http://www.ge - nomicmedicinealliance.org ), a founding partner of the", + "to understand the genetics of a variety of diseases andbiological systems including aging, the immune system and ironregulation [26,27,28,29,30]. Much of this work has been madeavailable through GeneNetwork (formerly WebQTL ) an on-line", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the" + ], + "answer": "GeneNetwork.org aids in finding the genetic causes of common diseases by utilizing a systems genetics approach. This approach allows researchers to explore which phenotypes and diseases result from diverse sets of genetic and molecular markers, rather than focusing on single gene mutations [1]. GeneNetwork provides access to a wide variety of data, including genotypes (e.g., SNPs) and phenotypes, which can be used to study complex traits [7]. The platform integrates data on gene expression, protein interactions, and DNA-protein binding to construct biological networks that are predictive of disease [4]. Additionally, GeneNetwork offers correlation and mapping strategies for assessing associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [5]. This makes GeneNetwork a powerful tool for predictive medicine and systems genetics, helping researchers understand the genetic underpinnings of common diseases [6].", + "question": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_5 new file mode 100644 index 0000000..f4fd1c4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Genome-wide polygenic scores for common diseases.pdf", + "2018 - Communication of cancer-related genetic and genomic information A landscape analysis of reviews.pdf", + "2012 - Population-based screening in the era of genomics.pdf", + "2009 - From Disease Association to Risk Assessment.pdf", + "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "2010 - Considerations for the Impact of Personal Genome Information.pdf", + "2004 - The emergence of epidemiology.pdf" + ], + "extraction_id": [ + "df306ee1-389f-56bb-bc5e-80ca8ff68fff", + "672e1f6a-25dd-5973-b19e-8d9371ec8973", + "706f0647-f63c-5383-9167-724c83faf79c", + "6f819601-6eea-54a4-ab88-27e1b0602287", + "37a4db8f-72a7-5e4e-b396-94bc0532a29d", + "3b79395f-0e1c-564c-9965-b04acf204132", + "074c3cae-ea97-5e74-8607-74c099df35cd", + "a58546e6-fe89-5d04-8adb-08d1991dc53c", + "694d74ca-68c2-5874-b143-113c6cc7802a", + "d7c4830a-8d69-531b-855a-eda3fa2ea5e7" + ], + "document_id": [ + "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "2a560126-b122-55dc-a213-a16bc00300b7", + "3a8d8722-9a3a-5062-9548-48e3c3bd6247", + "a61066d0-0d1a-5f10-96c3-aa96bacdad5e", + "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "b2665466-da66-59f0-8581-a68131e924bf", + "b2665466-da66-59f0-8581-a68131e924bf", + "c37e2ace-171b-5776-8969-86eda9736481", + "e4f3c9ce-1cc1-56f1-a290-b874455e53f9", + "53cc9020-d5d9-5c5b-a8df-66b3f1019961" + ], + "id": [ + "chatcmpl-AIGlUj81qIxsp6ZB9jZOhAF3uBNfA", + "d31fc0e8-028a-5879-b262-ec03ca586488", + "d57998c0-b045-5f68-a2ad-2173add21137", + "65aa608a-7e60-54bb-a299-ae1e2e66d0cd", + "deab786b-11ed-5c75-8ff5-fd2812138917", + "946c47ae-bbaf-5151-88f0-afa898c28a66", + "563b865d-03a4-5607-a6c5-a0ee977010b4", + "8ac717f0-586c-5ee3-b4e3-4334657938b5", + "f8a32960-cfe3-5440-9d5c-b55dfe52ea6d", + "3c0229cb-f853-5ef6-b45f-5462f62ede91", + "60497a7a-5c86-51a3-bc73-e373ca716270" + ], + "contexts": [ + "Letters NATure GeNeTicsIn our testing dataset, 19.8% of participants were at threefold increased risk for at least 1 of the 5 diseases studied (Table 2). The potential to identify individuals at significantly higher genetic risk, across a wide range of common diseases and at any age, poses a number of opportunities and challenges for clinical medicine. Where effective prevention or early detection strategies are available, key issues will include the allocation of attention and", + "genetic risks of disease on risk-reducing health behaviour: Systematic review with meta-analysis. BMJ. 2016;352:i1102. 57. Vernarelli JA. Impact of genetic risk assessment on nutrition-related life- style behaviours. Proc Nutr Soc . 2013;72(1):153159. 58. Marteau TM, French DP , Griffin SJ, et al. Effects of communicating DNA- based disease risk estimates on risk-reducing behaviours. Cochrane Database Syst Rev . 2010;(10). 59. National Human Genome Research Institute. All about The Human", + "personalized screening based on age and polygenic risk profile. 12 Pashayan N, Pharoah P. Translating genomics into improved population screening: hype or hope? Hum. Genet. 130(1), 1921 (2011). 13 Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31(1), 3336 (2002). nn\t Examines the potential for prediction of risk based on common genetic variation and compares this with the prediction that", + "Eur J Hum Genet. 12. Janssens AC, van Duijn CM (2008) Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet 17: R166173. 13. Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 17:15201528. 14. Wray NR, Goddard ME, Visscher PM (2008) Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev 18: 257263. 15. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE (2009)", + "within the general population and toutedfor its potential contribution to personal-ized medicine (1315), although the un-derlying clinical utility has yet to bedemonstrated (16,17). Given the poten-tial for individual genetic risk to beempirically quantied and rapidly com-municated, it is of interest to both clini-cians and the general public to discover ifmodiable characteristics like diet canmitigate risk in individuals empiricallydened as high risk on the basis ofgenotype.", + "Comprehension of Genomic Risk for Diabetes Public Health Genomics 2014;17:95104 DOI: 10.1159/000358413103 9 Green MJ, Peterson SK, Baker MW, Harper GR, Friedman LC, Rubinstein WS, Mauger DT: Effect of a computer-based decision aid on knowledge, perceptions, and intentions about genetic testing for breast cancer suscep-tibility: a randomized controlled trial. JAMA 2004; 292: 442452. 10 Bernhardt JM, McClain J, Parrott RL: Online", + "Comparison of family history and SNPs for predicting risk of complex disease. PLoS Ge-net 2012; 8:e1002973. Downloaded from http://karger.com/phg/article-pdf/17/2/95/3426597/000358413.pdf by guest on 03 July 2023", + "Genetics Home Reference - Genetics Home Reference provides consumer-friendly information about the effects of genetic variations on human health. http://ghr.nlm.nih.gov/ Gene Reviews Features expert-authored, peer-reviewed, current disease descriptions that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "Khoury, M. J. (2006). Family history of type 2 diabetes: apopulation-based screening tool for prevention? Genetics in Medicine, 8 (2), 102 108. Hunter, D. J., Khoury, M. J., & Drazen, J. M. (2008). Letting the genome out of the bottle will we get our wish? The New England Journal of Medicine, 358 (2), 105 107. Ioannidis, J. P. A. (2009). Personalized genetic prediction: too limited, too expensive, or too soon? Annals of Internal Medicine, 150 (2), 139141.", + "genomic profiling for measuring susceptibility to common diseasesand targeting interventions. Genet Med 2004; 6:3847. 42Vineis P, Christiani DC. Genetic testing for sale. Epidemiology 2004; 15:35. 43Haga SB, Khoury MJ, Burke W. Genomic profiling to promote ahealthy lifestyle: not ready for prime time. Nat Genet 2003; 34:34750. 44Yang Q, Khoury MJ, Botto L et al. Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes.Am J Hum Genet 2003; 72:63649." + ], + "answer": "The context provided does not specifically mention GeneNetwork.org or its capabilities in predicting health conditions based on genetic information. However, the context does discuss the general potential of genetic information to predict disease risk. For example, it mentions the ability to identify individuals at higher genetic risk for common diseases [1], and the potential for genomic profiling to measure susceptibility to diseases [10]. While these references indicate that genetic information can be used to assess disease risk, there is no direct mention of GeneNetwork.org's specific capabilities in this area. Therefore, based on the provided context, it is unclear if GeneNetwork.org itself offers such predictive services.", + "question": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_6 new file mode 100644 index 0000000..85d09e5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "28892088-5a95-56eb-822d-b12da3a612d0", + "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "dc001755-2e77-5b41-8617-263b3ba35af8" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "73724c99-98df-53b2-a378-29c8b4faa171", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194" + ], + "id": [ + "chatcmpl-AIGlYq5ocxHDndnTkp4uj3sxjmcHK", + "c63cfaee-749e-547b-9c0a-086266f10670", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "21936758-94b1-506f-9229-77e26001ae44", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "7b626f27-5aed-5464-a20d-463954ff057d", + "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "312eae52-ede7-5c13-8974-fce0126426cf", + "251de62d-6e8e-50c7-9616-7fea05a250fb" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "distributed neuroscience data sharing with ever expanding prospects for future breakthroughs. GeneNetwork.org : genetic analysis for all neuroscientists Originally named webqtl, GeneNetwork.org is the oldest contin- uously operating website in biomedical research ( Williams, 1994). This massive database contains ;40 million datasets. GeneNetwork.org also offers a powerful statistical platform for online network analyses and mapping, enabling numerous mo-", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "abundance data sets directly within GeneNetwork's ana- lytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (bar- ley) in a database that has been designed for an animal model species (mouse) with well established genome" + ], + "answer": "GeneNetwork.org utilizes data from different populations around the world by integrating diverse genomic information and phenotypic data from various experimental crosses and reference panels. This allows for comprehensive exploratory and statistical analysis of large published phenotype and genome datasets [3], [4]. The platform includes data from multiple species, which facilitates the comparison of gene expression patterns with drug responses and other phenotypic traits [3]. Additionally, GeneNetwork.org provides analytical tools that enable users to compare traits across datasets from different experimenters, further enhancing the ability to study correlations and perform data mining in genomic regions [5], [9]. This integration of diverse datasets supports the construction of predictive biological networks by interfacing DNA variation data with gene expression, protein interactions, and DNA-protein binding information [6].", + "question": "How does GeneNetwork.org make use of data from different populations around the world?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_7 new file mode 100644 index 0000000..a8a3e28 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf", + "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf" + ], + "extraction_id": [ + "d1c32c32-42c8-5065-b7f2-bd2a0baeae62", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "98aff04d-a5b2-5cca-bc1a-552055a74262", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6", + "2455cf6d-4c9b-5272-8650-da127cc329e8" + ], + "document_id": [ + "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "d235d186-3d1c-5cde-90d5-9c140cd920f4", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "73724c99-98df-53b2-a378-29c8b4faa171", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "76a715a4-8222-598b-8e65-6d5b6e807989", + "301d6469-2a9c-5960-88ac-8437212d78ab", + "67e804db-8127-5938-8d7f-a5918cdf4f86" + ], + "id": [ + "chatcmpl-AIGlcT3nIUJnhMWAFTwtm7j28RbUR", + "bd2eb0ef-24a1-55ff-8597-c21dff0ecf0a", + "c63cfaee-749e-547b-9c0a-086266f10670", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "23de1e96-55b6-5062-a2e1-02bf06fd3565", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "f5c218f0-1280-55f8-912b-b32b833e93a3", + "312eae52-ede7-5c13-8974-fce0126426cf", + "21936758-94b1-506f-9229-77e26001ae44", + "40ecbff2-5039-57f0-a01e-9d412d559dbe", + "18c7c27b-b51f-5ab6-9d09-4235c57811b1" + ], + "contexts": [ + "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table , and throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID= sharinginfo&GN_AccessionId=600 . GeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/ genenetwork/genenetwork1 .", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The strains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic correlations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15]. All data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16]. GN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "genetics approaches can not only provide insights into the roles of individual genes or developmental pathways but also illuminate relationships between different levels of a biologic system, such as the genome, transcriptome, and phenome [ 10]. One such resource of systems genetics is the GeneNetwork website and resource (www.genenetwork.org ) that provides access to a wide variety of data such as genotypes (e.g., SNPs), phenotypes that are obtained" + ], + "answer": "GeneNetwork.org provides a variety of genetic data, including: 1. Genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [3]. 2. Genetic variants such as SNPs (single nucleotide polymorphisms), insertions, deletions, and duplications [4]. 3. Extensive phenotype data extracted from the literature and submitted by users, which allows for comparisons of drug responses with gene expression patterns [5]. 4. Microarray data of gene expression in the brain and data of other phenotypes [8]. 5. Genotypes, including SNPs, and phenotypes obtained from various studies [10]. These datasets are designed to support systems genetics research and include data from multiple species [2], [5].", + "question": "What kinds of genetic data are available on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_8 new file mode 100644 index 0000000..efc389c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2017 - Analyses of differentially expressed genes after exposure to acute stress, acute ethanol, or a combination of both in mice.pdf" + ], + "extraction_id": [ + "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "308bef07-d720-5686-990d-d1e26a48e8a1", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "223e442e-898d-5aea-866a-5cdc0ac915e8", + "4f3d275e-f521-5ae9-b550-0411d2a1bb33" + ], + "document_id": [ + "76a715a4-8222-598b-8e65-6d5b6e807989", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "433904cc-23b8-50a5-ba84-0ee4d41d23c2" + ], + "id": [ + "chatcmpl-AIGljdYmj6PqUgXHWW6b3NFcoOufn", + "21936758-94b1-506f-9229-77e26001ae44", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "94f60899-c281-586e-8741-135a4fef2663", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "76ca1a96-ff40-515d-8d8b-5b1cde3c32b5", + "c63cfaee-749e-547b-9c0a-086266f10670", + "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "90151329-53f0-5d76-b428-da316848daf3" + ], + "contexts": [ + "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes are two such examples of possible tools. For this study, we", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "data are entered into GeneNetwork after they have been shepherded through a system like PhenoGen that has extensive capabilities for normalization and quality control. A comparison of the brain gene expression datasets and some of the tools for data analysis available on PhenoGen and GeneNetwork is shown in Table 3, and more detailed information on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5 Addict Biol . Author manuscript; available in PMC 2012 July 1.", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "by example in the Supplementary Methods, and in the Users Manual that can be downloaded from the website. There are a number of databases that investigators can use to assist in various aspects of gene expression data storage and mining (e.g., (Chesler et al., 2005; Galperin and Cochrane, 2009; Gentleman et al., 2004; Mailman et al., 2007; Saal et al., 2002; Swertz et al., 2010)). One relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler et", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "from co-regulation patterns found within tens of thousands of samples for which gene expression was measured. GeneNetwork provid es un- precedented resolution and predictive power across multip le cell types and tissues. Analogous to discovering patterns in expressi on data, the network of protein-protein interactions can also be comput ationally pre- dicted using various methods[381]. The combined current knowledge of how cells control functio ns", + "differentially expressed were further evaluated. Bioinformatic analyses were predominantly performed using tools available at GeneNetwork. org, and included gene ontology, presence of cis- regulation or polymorphisms, phenotype correlations, and principal component analyses. Comparisons of differential gene expression between groups showed little overlap. Gene Ontology demonstrated distinct biological processes in each group with the combined exposure (RSE) being" + ], + "answer": "Scientists use GeneNetwork.org to study differences in gene expression by leveraging a variety of analytical tools and datasets available on the platform. GeneNetwork provides access to large published phenotype and genome datasets from several species, allowing for exploratory and statistical analysis [2]. The platform includes microarray data of gene expression in the brain and other phenotypes, which can be used to compare traits across different datasets [1]. GeneNetwork also facilitates the comparison of gene expression patterns with drug responses and other phenotypic data, making it practical for identifying candidate genes for complex traits through QTL analyses [2], [4]. The platform supports correlation and network analysis to compare associations between tissues and across rodent or human datasets, which is useful for systems genetics mapping [5]. Additionally, bioinformatic analyses on GeneNetwork.org include tools for gene ontology, presence of cis-regulation or polymorphisms, phenotype correlations, and principal component analyses, which help in evaluating differentially expressed genes and understanding distinct biological processes [10].", + "question": "How do scientists use GeneNetwork.org to study differences in gene expression?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_9 new file mode 100644 index 0000000..839bc7b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_cs_gn_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf", + "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "2009 - High\u2010throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains.pdf", + "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf" + ], + "extraction_id": [ + "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "83ae495f-31a2-5977-a63a-57e704c394e2", + "22ed818f-78a7-5409-9f6a-1b83284db25d", + "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "308bef07-d720-5686-990d-d1e26a48e8a1", + "be1e859f-c4c7-576d-8a52-9588e15fab44", + "21f8c6e4-ef9b-582b-ac32-2679933c3b59", + "7a89fb44-80f8-5890-b2ae-a4643b587737", + "3ca48658-ca83-5952-8f8d-eb7ae491e6b6" + ], + "document_id": [ + "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "08b12d72-9776-5acb-b1ef-7ee402781897", + "99fc80f0-f3c3-5766-a604-921552bb3298", + "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "423c6929-9d69-5c95-b510-bff6757fed7d", + "301d6469-2a9c-5960-88ac-8437212d78ab" + ], + "id": [ + "chatcmpl-AIGlpL9doFmtJJWVoX5DvTMPsImCv", + "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "a1124460-ae34-57fb-846b-e033f4bbf49c", + "d45f4d61-dfd4-57ef-9b52-ae6cbff0e6f4", + "2b47c0db-8e09-51a2-8689-defa87ee8ac1", + "067136a5-b89e-5108-85b0-f638c041e68c", + "94f60899-c281-586e-8741-135a4fef2663", + "4b91e1d0-f7ce-577c-bad2-b59bd75173b0", + "2f453c67-3f97-5d7b-b92d-0530f86e26ee", + "c61e7911-9138-5a2e-8b2f-e035f374e9e3", + "40ecbff2-5039-57f0-a01e-9d412d559dbe" + ], + "contexts": [ + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "GeneNetwork.org is also a valuable teaching tool. While mainly designed for researchers interested in testing gene-to- phenotype relationships, GeneNetwork. orghas been adapted for dry-lab teaching in neuroscience and genetics ( Grisham et al., 2017 ). A useful approach is to assign sets of vetted questions, such as the exam- ples discussed above, and to help students work toward answers, solutions, or novelquestions. Several examples relating to the", + "Category 1: Web Resources for Online Analysis of the Genetics of Alcoholism and More GeneNetwork (www.genenetwork.org): This is a comprehensive resource for learning about genetics, but users may", + "GeneNetwork also features a phenotype database, a public repository of data from over 700 traits previously measured across several laboratories in BXD RI (and other) strains. These include behavioral, biochemical, and anatomical traits. The data consist of strain means, not raw data from individual mice, and so we use the term genetic correlation. Using this database, we performed correlation and network analyses to identify relationships with", + "biological function of the new gene list. As mentioned previously, GeneNetwork (www.genenetwork.org) is a collaborative Web-based resource equipped with tools and features for studying gene/gene and exploring genetic correlates to neurobehavioral phenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of gene expression and phenotypic data from a variety of species and brain regions, with a host", + "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites focus to a large extent on correlations of behavioral phenotype with gene expression levels in recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means to identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to the tools for raw expression data analysis described above, is that the user can not only", + "with another database, GeneNetwork, correlating behavioral phenotypes with geneO'Brien et al. Page 11 Int Rev Neurobiol . Author manuscript; available in PMC 2014 July 21. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript", + "interested in behavioral variation and in ways to exploit bioinformatic resources and methods to dissect and (we hope) reassemble and model behavior. You do not need to be a statistician or geneticist to use these tools. In order to use GeneNetwork, we have to start with some ground rules and assumptions. The first is that behavioral traits must vary significantly. This is a chapter about behavioral variation with an equal emphasis on both words. If a behavior is a \"fixed action pattern\" that", + "facilitated through the development of GeneNetwork(www.genenetwork.org), an Inte rnet resource for the multi- variate genetic analysis of complex traits in genetic reference populations (Chesler et al. 2003, 2004; Wang et al. 2003). GeneNetwork aids in identication of candidate genesand bio-molecular mechanisms underlying addiction-relatedphenotypes and includes a wealth of data on mRNAexpression proles from various tissues of the centralnervous system (Chesler et al. 2005; Peirce et al. 2006;", + "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other investigators can look for correlations between gene expression patterns and phenotypic traits. The GeneNetwork is an open resource and consists of a set of linked resources for systems genetics. It has been designed for integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the" + ], + "answer": "Yes, GeneNetwork.org can be used to learn about genetic influences on behavior. It is a comprehensive resource equipped with tools and features for studying genetic correlates to neurobehavioral phenotypes [5]. The platform includes a phenotype database with data on behavioral traits, among others, which can be used for correlation and network analyses to identify relationships with genetic data [4]. Additionally, GeneNetwork focuses on correlations of behavioral phenotypes with gene expression levels in recombinant inbred and inbred panels of mice and rats, which helps in identifying candidate genes for complex traits [6]. The resource is designed for the multivariate genetic analysis of complex traits, including behavior, in genetic reference populations [9].", + "question": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_1 new file mode 100644 index 0000000..4693a1d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "2015 - Single cell RNA-seq reveals changes in cell cycle and differentiation programs.pdf", + "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2022 - Parallel bimodal single-cell sequencing of transcriptome and methylome.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "2018 - Epigenetic Modifications in Cardiovascular Aging and Diseases.pdf" + ], + "extraction_id": [ + "7fcf2db7-a5cc-5ffe-aee1-98f447d9b8e5", + "f277b0c9-43b7-542a-828f-1bebcf0cddf9", + "6411b0be-b683-56dd-a3a5-e76618ede4e4", + "e6e9d12b-1784-5f0e-924e-442be1636afb", + "a0a95eb8-8214-5918-9b54-7f69eec9df53", + "60355441-16f5-53a2-9b24-9616624f8d00", + "1e4f0bd9-2e45-536b-ae84-33c3ed01ba34", + "548ece22-253e-512f-8dcd-7ffc6c95d482", + "4104881f-c452-54ba-b7ab-5c13f9dc8bad", + "7109021e-4b15-502a-a9d1-ef6055acdf9d" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "42f2b0ac-0991-58fb-bb24-128b368cc1d7", + "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "e112f7dc-0ca4-51df-82ed-2f9748f9a3b5", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "6559d6a2-bd88-5538-a611-da6288439bed" + ], + "id": [ + "chatcmpl-AIHWB476q1MhqSkzMBJiel5B4S1fK", + "91b0d8d4-949d-528f-b56d-8e5d99655fbc", + "27854d68-63aa-5b56-9580-46c83cd121db", + "f74570ce-e096-50b2-8061-b9a538299b3a", + "04c9d851-f9d9-53e6-b1ba-1bf3398395f5", + "f037fb19-8998-5f24-ab7c-b1ecc0e1b7ef", + "cdc7970a-4c9a-55c8-accb-8f99d88b0f6c", + "9c580ff4-e422-56e4-b774-44cbc2e5d87c", + "225b6504-a754-5aa3-ae95-b4019dbcaa8b", + "4a1ef9aa-4fbc-5093-9c53-73937397c715", + "1a51a565-e5bf-5659-84a2-39e06def18fd" + ], + "contexts": [ + "Single-cell sequencing has helped to support several hypotheses about the cel- lular and genetic origin of age-related dysfunctions. Since single-cell sequencing allows us to study small populations of cells, it has been possible to find low repre- sented mutations as well as transcriptional events that alter cellular identity. This newly generated data suggests that aging could be the result of mutational accumu- lation, epigenetic errors, and transcriptional noise that occurs in cells altering the", + "structed using data from bulk tissues, which neglect the variationsin cell compositions and cell-to-cell aging heterogeneity. To gain amore detailed and nuanced view of cell type specific molecular changes during aging, several studies have applied machine-learn- ing models to single-cell transcriptomics and DNA methylation", + "within whole tissues or individual cell types in aging (Rodwellet al. 2004; Jonker et al. 2013; Cosgrove et al. 2014; O Brown et al. 2015; Su et al. 2015; White et al. 2015; Keyes et al. 2016; Benayoun et al. 2019). However, it remains unclear to what degree age-related transcriptional changes are shared or unique across cellidentities. To address this outstanding question, we performed dif-ferential expression analysis within each cell identity betweenyoung and old mice.", + "populations. Furthermore, single cell analysis should allow us to relate prospective profiles of HSCs that have just been isolated with known heterogeneity in their retrospective functional capacity in transplantation assays. Here, we leveraged single cell RNA-seq to directly assess transcriptional heterogeneity within the HSCs and how it may change with age in the steady-state unperturbed hematopoiesis. Given that HSCs are", + "cells. Here, we used single-cell RNA-seq to investigate aging across a diverse set of murine cell identities in three tissues. We found that cell identities differentially express unique genes with aging, consistent with previous reports of cell-identi- ty-specific aging phenotypes (Angelidis et al. 2019). Similar celltypes (e.g., kidney capillary endothelial cells and lung endothelial cells) showed broadly similar aging trajectories across tissues, and", + "Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent dis-coveries", + "Genomics 114 (2022) 110379 2have been observed in multiple species and tissues [7,8]. Transcriptome analysis using aged oocyte samples have confirmed the impact of aging on transcriptome landscapes [9,10]. Advances in single-cell sequencing technology promote our understanding of intrinsic complexity to another level [11]. Recently, we have successfully applied single-cell transcriptome technique to reveal cellular and molecular transitions in", + "present in multiple tissues, such as endothelial cells andepithelial cells, also tended to belong to the same category acrosstissues ( Supplemental Fig. S23). These findings indicate that inherent characteristics of cell types play an important role in shaping cell aging patterns, even when situated in different tissue environments. Discussion Here we show that tissue-specific aging programs can be learnedfrom scRNA-seq data and applied to describe aging heterogeneity", + "creased in old lung stromal cells. Using matrix factorization andoptimal transport methods, we computed trajectories of agingfor each cell identity and assessed the influence of identity and en-vironment on these trajectories. Results Single-cell RNA-sequencing identifies a diversity of cell types and states in young and old mouse tissue We collected transcriptional profiles of young and old cells of many identities by isolating single cells from the kidney, lung,", + "during the last decades. However, different types of cells in the cardiovascular system may be highly heterogeneous dur - ing aging and disease progression. Single-cell genomics, such as massively parallel single-cell RNA-seq, facilitate detailed transcriptome analysis to identify variants of key epigen-etic enzymes/pathways in specific diseased cohorts or cell types. 54,57,58,146 Altogether, new sequencing technologies have" + ], + "answer": "Recent single-cell transcriptomics studies have significantly enhanced our understanding of cellular heterogeneity in aging tissues by providing detailed insights into the molecular and cellular changes that occur with age. These studies allow for the examination of small populations of cells, enabling the detection of low-represented mutations and transcriptional events that may alter cellular identity, supporting hypotheses about the cellular and genetic origins of age-related dysfunctions [1]. Single-cell transcriptomics has also been used to gain a more nuanced view of cell type-specific molecular changes during aging, which is not possible with bulk tissue analysis that neglects variations in cell compositions and cell-to-cell aging heterogeneity [2]. This approach has revealed that different cell identities express unique genes with aging, indicating cell-identity-specific aging phenotypes [5]. Furthermore, similar cell types across different tissues have shown broadly similar aging trajectories, suggesting inherent characteristics of cell types play a significant role in shaping aging patterns [5], [8]. Additionally, single-cell RNA-seq has been applied to study transcriptional heterogeneity within specific cell types, such as hematopoietic stem cells (HSCs), and how this heterogeneity changes with age [4]. This level of detail helps to understand the diversity of cell types and states in young and old tissues, providing insights into the intrinsic complexity of aging at the cellular level [9]. Overall, these studies are revolutionizing our understanding of cellular heterogeneity in aging tissues, opening new avenues for early disease detection and personalized treatments [6].", + "question": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_10 new file mode 100644 index 0000000..f6fa0d0 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Chromatin Remodeling, DNA Damage Repair and Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf" + ], + "extraction_id": [ + "d9a12bd9-c65e-547a-89aa-4e0231558ddc", + "e3e52327-4a23-5003-b418-dafdcdcae82c", + "3da50120-f7fd-5256-ae05-4ffd57876a5c", + "fdd9c5d5-2cca-5fe1-baed-c672f464dab0", + "a5caef7f-f1c1-55af-8807-3c9db425df7b", + "4cd1741b-c96f-592f-af69-95f3a10a157b", + "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "0a7a0a01-a262-51bf-bfaf-4f301a0a467b", + "93dbd5fc-d568-5b19-a9cd-fa192ed94ca7" + ], + "document_id": [ + "594e5dbe-b92a-5b0c-9f65-2a10670f9517", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82" + ], + "id": [ + "chatcmpl-AIHXSI5xx8VWq2TPqps22AUgX04Pq", + "42a07dfa-c5ac-535f-9a65-8c53b8f10aec", + "86bd9226-94dc-5186-984e-3dd140de9af3", + "79535f3c-51b2-5696-9081-3fdf146e8e61", + "6b4d2b61-4c6b-5b9e-a175-7a3c53a923a5", + "609e97e2-babd-5a49-9451-1a6162eb01e4", + "9fac0777-2bcb-528c-9c16-cbcd85e28522", + "b9de772a-53c5-5128-a595-9baf9420e534", + "1d1662ae-28d6-514d-a600-8860b061a504", + "43c4d87f-c0ce-5148-b601-77e6fd8956b2", + "0acc43f6-5d5b-53f5-af2f-53077b26591a" + ], + "contexts": [ + "Chromatin Remodeling, DNA Damage Repair and Aging Current Genomics, 2012 , Vol. 13, No. 7 539 Ercc1 also show premature aging phenotypes, providing evi- dence of a direct correlation between impaired DDR and premature aging [137, 138]. The relationship between DNA damage accumulation and aging has gained maximum credibility through studies", + "genome is being transcribed or replicated, the threshold of damage needed to activate DDRs, and the choice of cell fate in response to genotoxic stress. It is important to point out that cross-sectional studies, which are largely all we have to date, yield information about the burden of DNA damage and cannot inform as to whether lesions accumulate over time. Longitudinal studies on tissues that can be serially accessed are desperately needed. DNA Repair Capacity Decreases with Aging", + "INTRODUCTION Damage to DNA occurs with surprising frequency. DNA lesions can cause mutations, blocktranscription and replication, and trigger the DNA damage response (DDR). The DDR arrests cell cycle progression and activates signaling pathways that impact cell fate: repair, apoptosis, or cellular senescence. DNA damage is widely recognized as a cause of cancer, and strong evidencenow links DNA damage to aging and diseases associated with aging.", + "DNA damage and persistent DDR signalling as a shared causative mechanism of cellular senescence andageing. Curr. Opin. Genet. Dev. 26:8995 103. Rodier F, Coppe JP, Patil CK, Hoeijmakers WA, Munoz DP, et al. 2009. Persistent DNA damage signalling triggers senescence-associated inammatory cytokine secretion. Nat. Cell Biol. 11:97379 104. Garinis GA, Uittenboogaard LM, Stachelscheid H, Fousteri M, van Ijcken W, et al. 2009. Persistent", + "persistent DNA damage response (DDR) at telomeres and that even long telomeres may be a target for the accu-mulation of irreparable DNA damage. Therefore, DDR activation either at critically short telomeres or caused by persistent telomeric DNA damage represents the trigger of replicative cellular senescence or apoptosis 48, 50. The analysis of apoptosis by TUNEL assay showed that leukocytes from untrained T2D subjects were more sensitive to H", + "E) (2931) and have alleviated the dependency on invitro and invivo models by using direct human samples. AGe-ReLATeD DNA DAMAGe AND DNA DAMAGe ReSPONSe (DDR) ACTiviTY Age-related accumulation of DNA damage has been studied thoroughly, showing correlation between age and damage levels or mutation frequency (32, 33). In the presence of DNA lesions or abnormalities, the DDR, a complex multigenic pathway, is", + "Spontaneous damage is stochastic. But the response to DNA damage is highly conserved, geneti-cally controlled, and with evolution exceedingly more complex. DNA damage triggers activation of signaling pathways termed the DDR, which facilitates repair and arrests cell cycle progression until repair is complete. If DNA damage is extensive or irreparable, DDR effectors trigger celldeath (apoptosis) or cell senescence. These are potent tumor suppressor mechanisms. However,", + "to senescence. Genetic attenuation of the DDR enables reversal of cellular senescence (81). Incontrast, introduction of DSBs in mouse liver, using a tetracycline-inducible SacI restriction endonuclease system, increases the burden of senescent cells in vivo and triggers hallmarks of liver aging (82), illustrating a clear path for how DNA damage can play a causal role in aging. Markers of senescence are detected at higher levels in tissues of older mice, humans, and other", + "mechanisms. In general, it appears that DDR signaling enhances DNA repair and autophagy tocontrol the level of damage in the cell. Interestingly, evidence, albeit early evidence, has been found that DNA damage is linked to proteostasis. Expression of proteins containing polyglutamine tracts that drive protein aggrega- tion linked to neurodegeneration activates the DDR and H2AX foci (148). Interestingly, DNA breaks in cells and H2AX foci in brain of a murine model of Huntington disease are detected", + "its relevance to age -related functional decline at the molecular and cellular level. The importance of oxidative stress and key DNA damage response (DDR) pathways in cellular aging is discussed, with a special focus on poly (ADP -ribose) polymerase 1, whose persistent activation depletes cellular energy reserves, leading to mitochondrial dysfunction, loss of energy homeostasis , and altered cellular metabolism. Elucidation of the relationship between genomic instability ," + ], + "answer": "The DNA damage response (DDR) plays a crucial role in both replicative and chronological aging by maintaining genomic stability and influencing cell fate in response to DNA damage. Here are the key points regarding its significance: 1. **Premature Aging and DDR Impairment**: Impaired DDR is directly correlated with premature aging phenotypes, as evidenced by studies on certain genetic models like Ercc1 [1]. This suggests that a functional DDR is essential for normal aging processes. 2. **Cellular Senescence and DDR**: Persistent DDR signaling is a shared mechanism that triggers cellular senescence, which is a hallmark of aging [4]. This indicates that DDR not only repairs damage but also influences aging by promoting senescence when damage is irreparable. 3. **Replicative Senescence**: DDR activation at telomeres, especially when they are critically short or damaged, triggers replicative cellular senescence or apoptosis [5]. This highlights the role of DDR in controlling the replicative lifespan of cells. 4. **Age-related DNA Damage Accumulation**: As organisms age, DNA damage accumulates, and the DDR pathway becomes increasingly important in managing this damage to prevent mutations and maintain cellular function [6]. 5. **Tumor Suppression and Aging**: While DDR mechanisms like apoptosis and senescence are potent tumor suppressors, they also contribute to aging by removing or halting the proliferation of damaged cells [7]. Overall, the DDR is significant in aging as it balances repair and cell fate decisions, influencing both the replicative capacity of cells and the overall aging process by managing DNA damage and maintaining genomic integrity.", + "question": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_11 new file mode 100644 index 0000000..7281b2f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Structural, functional and molecular analysis.pdf", + "2007 - Immunosenescence comes of age.pdf", + "2020 - Age-related gene expression and DNA methylation changes in rhesus.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2022 - Immunity and lifespan answering.pdf", + "2007 - Immunosenescence comes of age.pdf", + "2012 - Pleiotropic Cellular Functions of PARP1 in Longevity.pdf", + "2007 - Immunosenescence comes of age.pdf", + "2007 - The skin as a mirror of the aging process in the human organism.pdf" + ], + "extraction_id": [ + "d9ef944b-b9a5-5b45-aaa6-c48f6fe54893", + "1ec3aae0-b171-511c-8250-fc0731aa3ec8", + "245e6d14-fa43-5af6-92d3-c5d7bf0235c2", + "1635dbe1-1dcb-5213-9446-74129d50c5f8", + "72b29fff-be72-5ede-85c9-7dc81894c956", + "b7467732-698f-5ca4-be08-08b011b0d343", + "1ec3aae0-b171-511c-8250-fc0731aa3ec8", + "f12b7e5c-29bc-5f56-9303-ab9286f22d88", + "170e6d89-2624-5b49-a6d1-95d4f35f73f3", + "daf4bb0f-4be5-5c47-baa5-686cd61adc1a" + ], + "document_id": [ + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "22313267-b0be-572f-8170-dcb814fe6140", + "0f1fe2f6-b9c8-514d-ac1c-4e7c07a19ff0", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "a834e7ee-7bab-5c4d-a236-b570d1ae635f", + "22313267-b0be-572f-8170-dcb814fe6140", + "e67324c0-474b-5280-8cbc-3778c6c0e5f0", + "22313267-b0be-572f-8170-dcb814fe6140", + "c429b80b-ad40-5fd3-b189-3982e5a8ab23" + ], + "id": [ + "chatcmpl-AIHXbfIiqBOfJAG67WB3RBf5qTOVk", + "65fe4bdc-890e-53bf-ad11-2d9c67adac7f", + "0c2a9ad8-054d-5a03-af43-704d2b7722d0", + "a8f4f7d2-85f9-5097-b588-614c7973c3b5", + "6822e1b6-b9bc-5e26-b6d5-d0d141854dd4", + "c0eedfc9-fd74-51f8-ace9-dfd79ad16b71", + "c4f7a0e2-0d13-5928-aaf2-8fc70dc9face", + "1683b89a-86bd-5439-9a6f-df120b67d0e8", + "fb4173c8-cf14-59d2-804c-3c2824a3fdc5", + "f16127b0-68dc-50bc-b39e-8ead81d723ee", + "ba9fdb3c-b9c2-57a2-9bb7-df5472d20e73" + ], + "contexts": [ + "immune system are one of the hallmarks of the aging body. Immunosenescence is the functional decline of the adaptive immune system brought on by natural agingwhereby protection against infection by pathogens and the effectiveness of vaccination decline [45,46]. The sec- ond aging-induced change in the immune system iscalled inflammaging which is characterized by a low- grade chronic inflammation process that contributes to", + "the increased susceptibility of the elderly to infectious disease and tothe poor outcome of vaccination. Defence against pathogens is com-promised mainly because of changes in adaptive immunity mediatedby T and B lymphocytes; however, all components of the immunesystem are affected (Fig 1). Dissecting the crucial alterations responsi-ble for dysfunctional immunity in old age will facilitate the develop-ment of rational interventions to reconstitute appropriate immunefunction. Given the increasing", + "[39] C. Castelo-Branco, I. Soveral, The immune system and aging: a review, Gynecol. Endocrinol. 30 (2014) 1622. [40] S.A. Johnson, S.J. Rozzo, J.C. Cambier, Aging-dependent exclusion of antigen-in - experienced cells from the peripheral B cell repertoire, J. Immunol. 168 (2002) 50145023 . [41] D.P. Shanley, D. Aw, N.R. Manley, D.B. Palmer, An evolutionary perspective on the mechanisms of immunosenescence, Trends Immunol. 30 (2009) 374381.", + "immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems. Age-relatedimmune decline also links to the concept of inflamm-aging, whereby aging is accompanied by sterile chronic inflammation. Along with a decline in immune function, aging is accompanied by a widespread of omics remodeling.", + "ence the development of inflamm-aging and immunosenes- cence phenotypes. Finally, although discussed studies have reported age-related changes in innate immune cell processes, there is still little known about how these changes are influenced by biologicalsex. Indeed, both the adult mammalian immune system [ 80,125] and the aging process [ 126] are sex-dimorphic, suggesting that", + "tion has also been implicated in ageing across a range of non-model organisms, including mice,nematode worms ( Caenorhabditis elegans ), and primates [ 4042]. The damage caused by the ageing adaptive and innate immune systems gives us insights into how these different arms of the immune system may in uence longevity. In general, adaptive im- mune function diminishes with age, whereas innate immune function is maintained [ 34,4346].", + "development to senescence, innate immunity to adaptive immunity,and genes to environments, in organisms ranging from mice to monkeys and humans. Understanding and eventually modulatingimmune dysfunction in the elderly now beckons. Lymphocyte development and ageing", + "an age-related decline in the capacity of adaptive immunity,consisting of more specic responses carried out by B andT cells [ 7]. Thus, with advanced age, the immune system undergoes a gradual remodeling in the attempt to reestablisha new balance that assures survival, however, favoring thedevelopment of chronic inammatory conditions [ 5,6,8,9]. DNA damage and inammation are inevitably linked by", + "All components of the immune system are altered as ageing pro-ceeds (Fig 1); however, the T-cell and B-cell compartments seem tobe particularly susceptible. The most severe clinical impact is proba-bly a result of the loss of diversity in the TCR and B-cell-receptorrepertoire, owing to the accumulation of dysfunctional cells, anddecreased thymic and bone-marrow output. Several interventionsdiscussed at the meeting could conceivably contribute to therestoration of appropriate immune function in the near", + "more susceptible to DNA damage. One of the major rea-sons are the impaired DNA repair mechanisms which havebeen described in several studies and have been associated with the initiation of age-associated diseases and progeroidsyndromes ( Hasty et al., 2003; Lieber and Karanjawala, 2004). Furthermore, dysregulated immune and inamma- tory responses have been already documented both inhumans and mouse with increasing age ( Badawi et al., 2004; Kovaiou et al., 2007 )." + ], + "answer": "Age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases through several mechanisms: 1. **Functional Decline of the Adaptive Immune System**: Immunosenescence is characterized by a decline in the adaptive immune system's function, which leads to reduced protection against infections and decreased effectiveness of vaccinations [1]. This decline is primarily due to changes in T and B lymphocytes, which are crucial for adaptive immunity [2]. 2. **Loss of Diversity in Immune Receptors**: There is a loss of diversity in the T-cell receptor (TCR) and B-cell receptor repertoire as people age. This is due to the accumulation of dysfunctional cells and decreased output from the thymus and bone marrow, which are essential for generating new immune cells [9]. This loss of diversity impairs the immune system's ability to recognize and respond to new pathogens effectively. 3. **Chronic Inflammation (Inflammaging)**: Aging is also associated with a state of low-grade chronic inflammation, known as inflammaging. This chronic inflammation can further compromise immune function and contribute to the development of age-related diseases [1], [4]. 4. **Overall Immune System Alterations**: All components of the immune system are affected by aging, not just the adaptive immune system. This widespread alteration can lead to a compromised defense against pathogens, making the elderly more susceptible to infectious diseases and less responsive to vaccinations [2], [9]. These changes collectively lead to an increased susceptibility to diseases in the elderly, highlighting the importance of understanding and potentially intervening in these age-related immune alterations to improve health outcomes in older populations.", + "question": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_12 new file mode 100644 index 0000000..29f4353 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2007 - Biological Aging Is No Longer.pdf", + "2018 - Human Ageing Genomic Resources new and updated.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Predicting age from the transcriptome.pdf", + "2019 - Improved precision of epigenetic clock.pdf", + "2011 - How pleiotropic genetics of the musculoskeletal system.pdf" + ], + "extraction_id": [ + "660d608e-8333-590f-8183-31b51779cec3", + "1af20df8-561f-59cb-9996-106a3be3f82f", + "f9312bd9-9f67-5e36-9986-f01d66d4b7ac", + "f9312bd9-9f67-5e36-9986-f01d66d4b7ac", + "5362f054-bb14-53fd-8d6d-9fb7aa41b3f3", + "62ff5c38-25a5-5729-a160-ce89e2ceb1c8", + "5a07784a-755c-598d-9d2d-3eb2ab8285cc", + "be79444e-743f-5289-9607-db6bc3b35493", + "6e048749-b423-54c0-9505-439db5595254", + "1b0806b9-729c-581f-9e3f-a98a5e0ce7eb" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "efef1c11-52f9-5b95-878a-07980080f0f8", + "82726cea-f77c-5a92-9f2e-ecccc369953a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "73128c69-30e0-5b7a-9504-1502e3f062c7", + "556d0179-023f-581f-9c2d-febe4e75722f", + "ed31486c-a651-5894-bd96-21fbd78f2646" + ], + "id": [ + "chatcmpl-AIHXkz3iFRslvxy1Jaw30l5EF9v8O", + "8139ed83-471f-5aa8-a6e1-2294b106ffd7", + "eeed3c27-9717-5592-8d69-937eca35bfff", + "b545cd47-00c7-5bd8-bd25-8d2bf59be62e", + "4b418218-07f6-5103-a9f4-4a28be7247c8", + "11d9e838-e4a1-50d4-92e8-658d4ff57b68", + "71a04373-81b9-5219-bbde-6f9cd1935491", + "ed814cb1-4fd3-5586-bd75-131d2a3ae96b", + "bb3a61fd-7137-5735-b65c-8aabab7eb971", + "c2ea0dae-b466-5c5b-babb-bfa74243bd34", + "96135704-e84c-53fc-9b57-b1e7b8dcd81f" + ], + "contexts": [ + "tifications of biological aging: do they measure the same thing? Am J Epidemiol. 2018;187(6):122030. 74. Putin E, etal. Deep biomarkers of human aging: application of deep neural networks to bio- marker development. Aging (Albany NY). 2016;8(5):102133. 75. Rehkopf DH, etal. Leukocyte telomere length in relation to 17 biomarkers of cardiovascular disease risk: a cross-sectional study of US adults. PLoS Med. 2016;13(11):e1002188.", + "studied (Table 13.1). Thus, due to the generation of these data and technological advances, possibly in the future, artificial intelligence programs will be able to reliably forecast the life of an individual, as well as the possible diseases that he may suffer in ageing; so these advances and discoveries will allow us to achieve a personalized medical treatment as a result of to the integration of biomarkers of ageing. Ageing Is aTreatable Condition", + "the data. However, construction of such models is often highlydegenerate, yielding little overlap of identified biomarkers be-tween studies and thus making results difficult to interpret(Thompson et al. 2018; Galkin et al. 2020). Among the many computational algorithms, linear regres- sion and its variants have been widely used to select aging-relatedbiomarkers and build aging clocks, namely, predictors of chro- nological age and biological age, in various omics data sets and ag-", + "states, which can be monitored using various biomarkers (Belskyet al. 2015). These markers are usually measurable indicators of aparticular outcome or source of aging, such as phenotypical mea-sures like frailty and molecular measures like DNA methylation dy- namics (Schumacher et al. 2021; Lpez-Otn et al. 2023). Although informative, they are not always quantitatively predictive of anindividual s true biological age, nor are they easy to obtain. The ad-", + "biomarkers of the aging process.", + "supervisedmachinelearningappliedtoageingresearch. Biogerontology ,18,171188. 47. Kriete,A.,Lechner,M.,Clearfield,D.andBohmann,D.(2011) Computationalsystemsbiologyofaging. WileyInterdiscip.Rev.Syst. Biol.Med. ,3,414428.Downloaded from https://academic.oup.com/nar/article/46/D1/D1083/4599180 by guest on 14 October 2023", + "associated with age, such as mouth width, nose width, and eye corner droop. This type of bioimage analysis has rendered relatively accurate calculations of the actual age, although this accuracy tended to fall with increasing age after 40years [71]. Integration ofBiomarkers ofAgeing Biomarkers of ageing allow estimating the biological age of an organism (Table 13.1) while providing information on their health status. Different studies are looking for", + "Background There is a marked heterogeneity in human lifespan and health outcomes for people of the same chronological age. Thus, one fundamental challenge is to identify mo- lecular and cellular biomarkers of aging that could pre- dict lifespan and be useful in evaluating lifestyle changes and therapeutic strategies in the pursuit of healthy aging. Here, we developed a computational method to predict biological age from gene expression data in skin fibro-", + "Background Ageing is a major risk for diseases and mortality [ 1,2]. Chronological age has been widely used as a marker of ageing due to ease and accuracy of measurement [ 1]. However, it is not necessarily a good predictor of biological ageing since individuals with the same chronological age can vary in health, especially in later life [ 3]. Therefore, researchers have attempted to search for biomarkers of ageing that can predict functional cap- ability at a later age [ 4,5]. In 2013, Hannum et al. and", + "discriminate between adverse aging-related events, such as frailty (Mitnitski et al. 2002 ), immobility (Simonsick et al. 2001 ), and propensity to fall (Lord et al.1994 ). There are additional considerations when choosing biomarkers to characterize aging. First, biomarkers measured at a given age are merely snapshots of important regulatory systems (Seeman et al. 2004 ); there is no information on system dynamics if each biomarker is measured only once. Having longitudinal" + ], + "answer": "Advancements in machine learning and artificial intelligence significantly aid in the identification of biomarkers for biological aging by enabling the development of predictive models and personalized medical treatments. These technologies allow for the integration and analysis of complex biological data, which can be used to forecast an individual's lifespan and potential age-related diseases, thereby facilitating personalized medical interventions [2]. Machine learning algorithms, such as linear regression and its variants, are employed to select aging-related biomarkers and construct aging clocks, which are predictors of chronological and biological age based on various omics datasets [3]. Additionally, computational methods have been developed to predict biological age from gene expression data, which can help in evaluating lifestyle changes and therapeutic strategies aimed at promoting healthy aging [8].", + "question": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_13 new file mode 100644 index 0000000..6f9a062 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Mechanisms of Vascular Aging.pdf", + "2012 - Genomics and Genetics of Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2022 - Functional genomics of inflamm-aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2020 - Age-related gene expression and DNA methylation changes in rhesus.pdf", + "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf" + ], + "extraction_id": [ + "bfeb5c38-4fa6-5df5-90ce-63204deba3a8", + "726bbaa2-97e8-5f62-a731-a1ba3cf1778f", + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "7f8f4ca0-9b27-55e3-a889-030af08dc84b", + "575a9f30-8504-5526-90e0-e558bfc29c02", + "fe270a46-7f2f-5a25-b98f-a782511801fb", + "14dbffca-9dc8-5d8c-bb23-98bc80b77e86", + "2836777b-037b-52e4-a160-9cb02dd98b92", + "245e6d14-fa43-5af6-92d3-c5d7bf0235c2", + "d3686eba-0aa4-5c56-b60d-bf76c3ab433b" + ], + "document_id": [ + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "3c2efc4d-b5a8-5843-be7e-44c3b52f3d9b", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "62b635c3-040e-512a-b016-6ef295308a1e", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "435dc081-e3d1-52c5-93a1-caa11206422f", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "0f1fe2f6-b9c8-514d-ac1c-4e7c07a19ff0", + "7de8d462-8a3c-5625-8cbb-374f3bb46425" + ], + "id": [ + "chatcmpl-AIHXpT1Oa9sduYt2d6yF1iu8bJvoN", + "c4c7b861-6d13-5814-818d-a79ddabd742c", + "9d96fdeb-3b94-57d2-8025-db47be7c52ad", + "e9ddeedc-70ba-516f-ad9b-77e2b45cd01f", + "415a6dd6-0e64-5aef-8561-289d728ad721", + "729ae0a3-95f3-50c7-8c00-d1ce0673ea08", + "571e50a8-c009-59a5-b01c-0f01c4b5e163", + "ab32705e-4e02-59ab-986d-4552a4a522b9", + "55a6fe97-29cd-5969-8ea8-3b350b8e0554", + "3914af93-b251-54ae-b7bf-9c8243a24f74", + "d2ce22fd-6c12-56cf-948d-fc6604cf0f23" + ], + "contexts": [ + "in the vascular system are considered in terms of their contribution to the pathogenesis of both microvascular and macrovascular diseases associated with old age. The importance of progeronic and antigeronic circulating factors in relation to development of vascular aging phenotypes are discussed. Finally, future directions and opportunities to develop novel interventions to prevent/delay age-related vascular pathologies by targeting fundamental cellular and molecular aging processes are presented. (Circ", + "pression of numerous mRNAs, some of which directly influence aging and age-related diseases. Jung and Suh describe what we know about the importance of microRNAs in aging and how this exciting new field is just starting to become explored. The last review in this special issue by Hou et al. brings things together nicely with a systems biology perspective of aging. In order to model the immense complexity of aging, we require systems-level approaches. This review describes how several", + "autoregulation of blood flow,218 vascular structural remodel- ing, atherogenesis,219 and angiogenic processes.220 The impact of circulating factors on aging phenotypes was also demonstrated by studies using mice with heter - ochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse. 221 Cerebromicrovascular density typically declines with ad-vanced age, 222 and there is initial evidence that circulating an-", + "components, particularly chemokines and cytokines, in theblood and tissues ( Villeda et al., 2011 ). In addition to illuminating the inuence of the systemic environment on cellular function,such heterochronic studies emphasize the potential role of envi-ronmental factors in rejuvenating aged cells. Molecular signatures of aging have been directly tested as", + "related diseases. Ageing Res Rev. 2018;47:21477. 115. Kumar S, Vijayan M, Bhatti JS, Reddy PH.MicroRNAs as peripheral biomarkers in aging and age-related diseases. Prog Mol Biol Transl Sci. 2017;146:4794. 116. Smith-Vikos T, Liu Z, Parsons C, Gorospe M, Ferrucci L, Gill TM, etal. A serum miRNA profile of human longevity: findings from the Baltimore Longitudinal Study of Aging (BLSA). Aging (Albany NY). 2016;8(11):297187.", + "in the endothelium and the VSMCs and specific disease pro-cesses. There is evidence that the senescence-associated se-cretory phenotype can also induce paracrine senescence and alter the function of neighboring cells, and the role of this mechanism in vascular aging should be further evaluated. The possibility of paracrine transmission of senescence from microvascular endothelial cells to parenchymal cells also requires further investigations. It should be noted that many", + "protein VSIG4 as a biomarker of aging in murine adiposetissue. Aging Cell 2020; 19:e13219. 128. Angelidis I, Simon LM, Fernandez IE, et al. An atlas of the aging lung mapped by single cell transcriptomics and deeptissue proteomics. Nat Commun 2019; 10:963. 129. Clark D, Brazina S, Yang F, et al. Age-related changes to macrophages are detrimental to fracture healing in mice. Aging Cell 2020; 19:e13112. 130. Tabula Muris Consortium. A single-cell transcriptomic", + "Ungvari et al Mechanisms of Vascular Aging 861 mechanisms of vascular aging and identify translationally relevant treatments for the promotion of vascular health in older adults. The same cellular and molecular aging processes that af- fect arterial vessels and capillaries also affect veins and the lymphatic/glymphatic system, likely contributing to various disease pathologies. Examples include the potential role of cerebral venules in neuroinflammation, Alzheimer disease, and cerebral microhemorrhages", + "et al., Plasma proteomic signature of age in healthy humans, Aging Cell 17 (2018). [17] D. Mari, P.M. Mannucci, R. Coppola, B. Bottasso, K.A. Bauer, R.D. Rosenberg, Hypercoagulability in centenarians - the paradox of successful aging, Blood 85 (1995) 31443149. [18] S.A. Phillips, The vasculature in cardiovascular diseases: will the vasculature tell us what the future holds? Prog. Cardiovasc. Dis. 57 (2015) 407408. [19] R.A. Gibbs, J. Rogers, M.G. Katze, R. Bumgarner, G.M. Weinstock, E.R. Mardis,", + "16Lidzbarsky et al. Genomic Instabilities, Cellular Senescence, and Aging Frontiers in Medicine | www.frontiersin.org April 2018 | Volume 5 | Article 104 177. Smith-Vikos T, Slack FJ. MicroRNAs and their roles in aging. J Cell Sci (2012) 125:717. doi:10.1242/jcs.099200 178. Lanceta J, Prough RA, Liang R, Wang E. MicroRNA group disorganiza- tion in aging. Exp Gerontol (2010) 45:26978. doi:10.1016/j.exger.2009. 12.009" + ], + "answer": "Recent discoveries regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes include the following: 1. The importance of progeronic (aging-promoting) and antigeronic (aging-delaying) circulating factors in the development of vascular aging phenotypes has been discussed. This highlights the role of systemic factors in contributing to age-related vascular pathologies and suggests potential interventions to prevent or delay these conditions by targeting fundamental cellular and molecular aging processes [1]. 2. Studies using heterochronic parabiosis, which involves connecting the circulatory systems of young and aged mice, have demonstrated the impact of circulating factors on aging phenotypes. This research provides initial evidence that circulating factors can influence cerebromicrovascular density, which typically declines with advanced age [3]. These findings underscore the significant role that systemic factors, including circulating microvesicles, play in influencing aging phenotypes, particularly in the context of vascular aging and potential rejuvenation strategies.", + "question": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_14 new file mode 100644 index 0000000..be0909b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Structural, functional and molecular analysis.pdf", + "2017 - Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Structural, functional and molecular analysis.pdf", + "2012 - Structural, functional and molecular analysis.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Blood-based epigenetic estimators.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf" + ], + "extraction_id": [ + "07a2b9a1-d683-568d-b2e6-c2cc1fcffba5", + "faae2e40-6de8-5285-8410-ac1ef5dac6ad", + "b2654364-b3e8-5e26-9664-d19ca8f5605e", + "c50b343b-3eef-548c-88cd-d5bda6605619", + "66edc533-58a4-5ad1-96c4-7e0c05462de5", + "d9ef944b-b9a5-5b45-aaa6-c48f6fe54893", + "307ac6d0-46d2-50e8-a618-d640136d4131", + "a0bb2ab8-44b4-5409-814c-22005b259479", + "062e4ac3-ef28-5bfa-be8c-770757083cfb", + "bca61863-81b3-5ef7-850d-10cc9577a9e1" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "448d68d1-19a8-5f4c-a48b-8d33597bd03b", + "62b635c3-040e-512a-b016-6ef295308a1e", + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "0e803003-d6e5-570e-a810-1aea89d7ea63", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "2673299f-21e5-5746-9c33-84b99b373355", + "4d082da4-fa48-5170-8147-c4fea47a5d4b" + ], + "id": [ + "chatcmpl-AIHXx0hXjoPni1lj2qiHnS6BLuSSU", + "1bcfcf33-d9b4-55b7-a384-bc8e08893a22", + "f4ec4435-00f7-5477-984a-68d1eff9e7a0", + "393bd8fc-14c6-5fc3-be3b-3ddf1c218531", + "0856bafc-06ce-5716-af52-f65dc3abfafe", + "3742fdda-bdba-5c09-bf7c-732b2554c5fe", + "bb367137-9186-53aa-8765-af837b7b4242", + "a6a78000-8744-5f89-bcbb-d26781ece651", + "39564137-871b-5464-b364-ba63cbf9cc31", + "7a775400-f8f2-5758-af40-b461adc83aa3", + "35f973f6-2ca0-5d89-98b2-8e28a67323c5" + ], + "contexts": [ + "the adaptation of the microbiota to the physiological changes of the long aging process. It has been demonstrated that the microbiota on this population maintains the health and promotes the survival. Additionally, a relationship between a healthy microbiota and longevity had been proposed [44]. A possible pathway is an immu- nological and metabolic regulation linked to the increase of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium [44, 45].", + "Marchesi JR, Falush D, Dinan T, Fitzgerald G, et al:Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci USA 2011, 108(Suppl 1):4586 4591. 21. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S, Donehower LA, Issa JP: Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res 2010, 20(3):332 340. 22. Englander EW: Gene expression changes reveal patterns of aging in the", + "microbiota present in infants, adults, and the elderly. Appl. Environ. Microbiol. 73, 77677770 (2007). 40. Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832R833 (2016). 41. Tremaroli, V. et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce long-term changes on the human gut microbiome contributing to fat mass regulation. Cell Metab. 22, 228238 (2015). 42. Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved", + "Therefore, research in the field has demonstrated that aging is a potential modi- fier of the composition and function of the human microbiome. Figure 9.3 shows the local composition of the microbiome in an average older adult. It can be seen that Bacteroidetes and Firmicutes species are the most prevalent in this age. Recent data has shown that older people hide a microbiota that differs in the type and number of microorganisms from that of younger adults [38]. Young people", + "related malnutrition. Furthermore, it has been shownthat aging can cause bacterial overgrowth in the smallintestine [16,17] and promote changes in microbial com- position in the colon [18-20]. In addition, reported age- related changes in DNA methylation of the mouseintestine [21] might play a role in the altered gene expression levels observed in the duodenum and colon of aging mice [22]. Together these observations demon-strate that although certain aspects of the aging intestine", + "detectable. Changes in the gut microbiota in terms of compos- ition and functionality during the process of aging have previously been reported [19,20,51] and it hasbeen postulated that these changes might contribute to the development of immunosenescence and inflam- maging [18,52]. To establish whether the enhanced expression of genes playing a role in the immune sys- tem are due to modifications in the microbiota wemeasured the total number of all bacteria and of the", + "37. Li H, Qi Y , Jasper H.Preventing age-related decline of gut compartmentalization limits micro- biota Dysbiosis and extends lifespan. Cell Host Microbe. 2016;19(2):24053. 38. Mihajlovski A, Dor J, Levenez F, Alric M, Brugre J.Molecular evaluation of the human gut methanogenic archaeal microbiota reveals an age-associated increase of the diversity. Environ Microbiol Rep. 2010;2(2):27280. 39. Quercia S, Candela M, Giuliani C, Turroni S, Luiselli D, Rampelli S, etal. From lifetime to", + "[26], but at advanced ages, dramatic changes in its composition are associated with various diseases and frailty [27, 28]. Regarding pathological processes, it is known that cancer, obesity, diabetes, and inflammatory bowel disease (IBD) are associated with specific microbial alterations [29, 30]. In older ages, a burden of intrinsic and extrinsic factors affects the compo- sition of the microbiome and plays a determining role in every tract and tissue. Such mentioned factors can be seen in Fig.9.2.", + "Osawa R. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 2016;16:90. 14. Dugue PA, Bassett JK, Joo JE, Jung CH, Ming Wong E, Moreno-Betancur M, Schmidt D, Makalic E, Li S, Severi G, et al. DNA methylation-based biological aging and cancer risk and survival: pooled analysis of seven prospective studies. Int J Cancer. 2018;142(8):1611 9. 15. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA", + "survival advantage that is age- and site-specific: Results from a large multi-site study. Aging Cell 18, e12905 (2019). [PubMed: 30801953] 51. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep. 1, 134 (2011). [PubMed: 22355651] 52. Morrison KE, Jaarevi E, Howard CD & Bale TL Its the fiber, not the fat: significant effects of dietary challenge on the gut microbiome. Microbiome 8, 15 (2020). [PubMed: 32046785]" + ], + "answer": "Changes in the gut microbiome composition are closely linked to aging and longevity. As individuals age, the composition and function of the gut microbiome undergo significant modifications. These changes are thought to contribute to various age-related processes, including immunosenescence and inflammaging, which are associated with the aging immune system [6]. Research has shown that a healthy microbiota can promote survival and is linked to longevity. Specifically, certain bacterial families such as Christensenellaceae, Akkermansia, and Bifidobacterium have been associated with immunological and metabolic regulation, which may contribute to increased lifespan [1]. Additionally, the gut microbiota of older adults differs in type and number of microorganisms compared to younger adults, with Bacteroidetes and Firmicutes being the most prevalent species in older individuals [4]. These changes in microbial composition can be influenced by both intrinsic and extrinsic factors, which play a significant role in the health and function of the microbiome as people age [8]. Overall, maintaining a healthy gut microbiome is crucial for promoting longevity and mitigating some of the negative effects associated with aging.", + "question": "How do changes in the gut microbiome composition correlate with aging and longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_15 new file mode 100644 index 0000000..9028c2d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2004 - Linking nutrition to genomics.pdf", + "2008 - The Aging Brain.pdf", + "2007 - The role of nuclear architecture.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2001 - The genetics of aging.pdf", + "2009 - Genes and gene expression modules associated with caloric.pdf", + "2010 - Genetic Dissection of Dietary Restriction in Mice Supports the Metabolic Efficiency Model of Life Extension.pdf", + "2004 - A Transcriptional Profile of Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf" + ], + "extraction_id": [ + "5d1905a8-536e-5efd-a92b-21053093d956", + "b193c536-dc9e-5ea6-9a01-064243a6cbf3", + "f63ffca5-2418-5683-9958-558c46b48def", + "9655c555-838e-5cdf-85cf-13736c3cf028", + "5745c701-a549-51c3-adcc-b19c47436740", + "713d3122-d856-5dbc-a3bf-d8cd836830cb", + "0b45ae60-562c-5e48-a1c1-9eb29614a63c", + "da7abebd-f7c0-5b9c-b0f2-e29871326855", + "b382fe8a-0267-5515-ac4b-07be55420040", + "fddca610-97a6-5f2c-88b4-dc6e96c60cf3" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "99891ef7-0589-5c41-a61f-1ab1fe1c8939", + "874f5d02-35c9-5233-8ded-6e06c7570ca9", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "893ba204-2e69-563f-9046-7246ca61494f", + "92419d8a-27ed-5142-8a87-189c1ba5459b", + "4ab656a7-9656-526b-94e1-422875409b44", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3" + ], + "id": [ + "chatcmpl-AIHY3hgOmiQgttq4BdrpX79X5LkzF", + "b516b1a9-d0f2-5d1e-9015-4799c902770b", + "6870f741-be38-5d34-aafd-25da39e1ff68", + "c5b37b9a-1ffa-516b-9681-22fecc5aee5b", + "e01c4c58-342d-5369-89e6-98344af55000", + "b990eb0a-709a-500c-836e-83e202e0d6a6", + "ffe5fc40-f6d4-5066-9e07-424f7b8e3dc9", + "2b081115-d36e-57ec-aedc-2fd9691bc5e9", + "03196bec-4ae2-5408-b90c-12dcb38e5831", + "2cf68c41-aa60-5dca-8aa1-04bc0d7a4db3", + "51a448cf-6015-53f7-a949-f247b71efcef" + ], + "contexts": [ + "Metabolism Studies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates. It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases. Transcriptional profiles are currently being applied and investigated. One of them is a caloric restric-", + "Keywords: caloric restriction; hepatic expression profiling; lifespan prolongation; metabolic signaling;microarray analysis; nutrition response. Introduction", + "(154, 155). Caloric restriction has been shown to sig- nicantly increase life span and promote resis-tance to a broad range of age-related pathol-ogy in worms, ies, and mice. Some of theeffects of caloric restriction may be mediatedthrough the sirtuin family of genes, as exem-plied by SIR2, which prolongs life span in", + "Calorie restriction, a dietary regimen that extends the lifespan of numerous organisms, also delays the majority of age-related gene-expression changes in mice and, to a certain extent, in flies45,50. It is currently unclear whether the effect of calorie restriction on gene expression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest that there may be a causal link: Sir2 not only facilitates heterochromatin and promotes DNA stability, but is", + "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science 289:21262128. Mair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary restriction and death in Drosophila. Science 301:17311733. Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126:913922. Mathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap- proaches. Mech Ageing Dev 127:584589. Meric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "that caloric restriction also regulates mammalian aging, perhaps via the modulationof insulin-like signaling pathways. The nervous system has been implicated as a keytissue where insulin-like signaling and free radical protective pathways regulate lifespan inC. elegans andDrosophila . Genes that determine the life span could act in", + "extension by dietary restriction. Annu Rev Biochem 2008, 77:727-54. 8. Harper JM, Leathers CW, Austad SN: Does caloric restriction extend life iin wild mice? Aging Cell 2006, 5:441-9. 9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the effect of caloric intake on mortality in mice. FASEB J 2003, 17:690-2. 10. Spindler SR, Mote PL: Screening candidate longevity therapeu- tics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "Corton JC, Apte U, Anderson SP, Limaye P, Yoon L. Mimetics of caloric restriction include agonists of lipid-activated nuclear receptors. J Biol Chem 2004;279:4620446212. [PubMed: 15302862] Ferguson M, Sohal BH, Forster MJ, Sohal RS. Effect of long-term caloric restriction on oxygen consumption and body temperature in two different strains of mice. Mech Ageing Dev 2007;128:539545. [PubMed: 17822741] Forster MJ, Morris P, Sohal RS. Genotype and age influence the effect of caloric intake on mortality in", + "A key question still unresolved is to what extent the mechanisms of aging are conserved between species with vastly different lifespans. Some studies suggest that similar mechanisms are involved in aging in many species. Forexample, caloric restriction extends lifespan in yeast, worms,ies, mice, and primates (Weindruch 2003). Additionally,signaling through the insulin-like growth factor pathway,chromatin regulation by sir2,and oxidative damage have each", + "10.1111/acel.12103 241. Edwards AG, Donato AJ, Lesniewski LA, Gioscia RA, Seals DR, Moore RL. Life-long caloric restriction elicits pronounced protection of the aged myocardium: a role for AMPK. Mech Ageing Dev. 2010;131:739 742. doi: 10.1016/j.mad.2010.09.007 242. Colman RJ, Beasley TM, Kemnitz JW, Johnson SC, Weindruch R, Anderson RM. Caloric restriction reduces age-related and all- cause mortality in rhesus monkeys. Nat Commun. 2014;5:3557. doi: 10.1038/ncomms4557" + ], + "answer": "Caloric restriction extends lifespan across various species through several key molecular mechanisms: 1. **Sirtuin Activation**: Caloric restriction may exert some of its effects through the sirtuin family of genes, particularly SIR2, which is known to prolong lifespan in organisms like yeast, worms, and flies [3], [4]. Sirtuins are involved in chromatin regulation and promoting DNA stability, which are crucial for maintaining cellular health and longevity [4]. 2. **Insulin-like Signaling Pathways**: In mammals, caloric restriction is thought to modulate aging through the insulin-like signaling pathways. This mechanism is also observed in organisms like C. elegans and Drosophila, where it plays a role in regulating lifespan [6]. 3. **Oxidative Stress Reduction**: Caloric restriction is associated with reduced oxidative damage, which is a significant factor in aging. This reduction in oxidative stress is a common mechanism observed across different species [9]. 4. **AMPK Activation**: In mammals, caloric restriction has been linked to the activation of AMP-activated protein kinase (AMPK), which plays a role in energy homeostasis and has protective effects on the aged myocardium [10]. These mechanisms highlight the complex interplay of genetic and metabolic pathways through which caloric restriction can extend lifespan across diverse species.", + "question": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_16 new file mode 100644 index 0000000..cbb959e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2007 - Trends in oxidative aging theories.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2003 - Life-long reduction in MnSOD activity results.pdf", + "2020 - Growth differentiation factor 15 protects against the aging\u2010mediated systemic inflammatory response in humans and mice.pdf", + "2002 - Human mitochondrial DNA with large deletions.pdf", + "2011 - Mitochondrial complex I.pdf", + "2000 - Genome-wide study of aging and oxidative stress.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Oxidative Stress, Mitochondrial Dysfunction, and Aging.pdf" + ], + "extraction_id": [ + "9994d4e6-e53d-5381-af9c-e811afe7a802", + "6dcd5550-7f8d-5668-bb82-b6040cbf1e61", + "b934a2a9-a672-5d65-9d0d-bbc36652a148", + "f0a1875a-9969-598b-a670-e6f61bf11898", + "cebd8a1c-01ea-5c43-a2f1-96ea3c304259", + "14f137b3-20cf-5b34-a3dd-4b550a3dec92", + "c195a6a2-d6a9-53f3-a0dd-abe76ae29588", + "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "7f1594a3-120c-5982-aa4d-babd6ab70265", + "32c4c0b2-d44c-5121-8975-196040fb2a1d" + ], + "document_id": [ + "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "0cef9dec-dbbe-5b5d-bb43-1a21a601fde2", + "0ceff9cf-2b2b-5fe8-b844-f3f8ee7704ad", + "35de1e32-95eb-5b1d-acf9-2c37ea1cc3c4", + "6943c112-611d-5108-9d0f-d52c1138871b", + "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "24277eba-69dd-5e12-9aa4-bbb6f0a88f52" + ], + "id": [ + "chatcmpl-AIHY9RBdJPzHPCH0uE5dG6bbj0z6D", + "b39d86ef-3c6a-561f-b8eb-f90ac124c12c", + "091ca29b-5c85-5d0d-8fbb-e829bb71bd0c", + "69365543-2760-5376-8e90-9a922a9759a7", + "9713b3c5-cd67-57d1-8c17-b3a4db7f911f", + "4bab1bd2-05a4-5c8e-897d-e456be8c8998", + "d99e64c1-2fe1-50c5-8a75-a2390ed0eac0", + "0f1d7692-a2c0-5def-9545-c2c16019536e", + "fec5b83b-cd2c-51ea-83c9-45efdcbff83d", + "cbfc2dc4-99ae-5177-955f-4bc243689419", + "6d58996a-1250-5eaa-bc6f-bd1057ccca88" + ], + "contexts": [ + "under normal physiological conditions because of an imbal-ance between prooxidants and antioxidants. The imbalanceleads to a steady-state accumulation of oxidative damage in avariety of macromolecules t hat increases during aging, resulting in a progressive loss in the functional efficiency ofvarious cellular processes. In a recent review, Beckman andAmes made a useful addition to this debate by dividing the", + "tributing to impaired bioenergetics in aged cells include oxida-tion/nitration of mitochondrial proteins, destabilization of the macromolecular organization of electron transport chain com-plexes, and impaired mitophagy (a mitochondria-specific form of autophagy). The combination of increased mitochondrial Figure 2. Proposed scheme for mechanisms and pathological consequences of age-related oxidative stress in vascular endothelial cells. The", + "over the years to become the oxidative stress theory of aging, but the principle is the same, inthat the accumulation of oxidative damage drives aging. In support of this theory, a large body of literature indicates that oxidative damage to all cellular macromolecules increases with age. Furthermore, overexpression of antioxidant enzymes that detoxify ROS, such as copper- andzinc-containing superoxide dismutase (SOD), manganese-containing SOD, or catalase, increase", + "predicted from the oxidative stress theory of aging. Thistheory,whichisbasedonthetenetthatdamagecausedbyROSplays a critical role in determining life span, has been one ofthe most popular theories to explain the deterioration in bio-chemical and physiological processes that occur during theaging process. A large number of studies have producedcorrelative data in support of this theory, e.g., an increase inoxidativedamagetolipid,protein,andDNAwithagehasbeendemonstrated in a variety of tissues and organisms", + "during\tthe\taging\tprocess\t(Yi,\tChang,\t&\tShong,\t2018).\tOxidative\tdam - age to cellular macromolecules, or stress arising from mitochondrial DNA\t(mtDNA)\tmutation\tand\tincreased\treactive\toxygen\tspecies\t (ROS),\tis\ta\tkey\thallmark\tof\taging\tphysiology\t(Yi\tet\tal.,\t2018).\tAlthough", + "radical theory of aging, which argues that oxidative damageplays a key role in senescence. Among the numerousmechanisms known to generate oxidants, leakage of super-oxide anion and hydrogen peroxide from the mitochondrialelectron transport chain are the chief candidates. Increased damage to mtDNA could exacerbate this leakage of reactive oxygen species (ROS) (4). It is not known how mtDNA deletions accumulate during", + "most plausible explanation for aging. But, as we have discussed, not all types of damage contribute equally to aging. From this point of view, it seems that ROS generated by complex I (at sulfur iron clusters or flavin sites) may damage specific targets that can alter homeosta - sis in a significant enough way to influ - ence aging. The most obvious target for this damage is mtDNA. The generation of ROS specifically by complex I corre - lates with levels of oxidative damage in mtDNA.", + "increase lifespan also confer resistance to oxidative stress (1).This finding supports the free-radical hypothesis of aging, whichsuggests that reactive oxygen species that accumulate withincreasing age cause oxidative damage to macromolecules (in-cluding nucleic acids, proteins, and lipids) and are causally linkedto aging and death (8, 9). Free radicals have been found toregulate the expression of a number of genes that includeantioxidant defense genes involved in repairing oxidative dam-age, as well as", + "Molecular Biomarkers forOxidative Stress There are many theories that try to explain the nature of aging; however, none of them can explain every aspect of the biology of aging. One of the most accepted and studied is the one proposed by Denham Harman in 1956. This theory proposed that during lifespan organisms accumulate oxidative damage in their biomolecules. Oxidative damage is generated by reactive oxygen species (ROS), which are the", + "production by mitochondria and increased 8-oxo-dG con-tent in the mtDNA are frequently detected in aged tissues [40,4750], suggesting that progressive accumulation of oxidative DNA damage is a contributory factor to the agingprocess. Consistently, many studies have found that increasedoxidative damage in cells is associated with aging [ 5153]. Furthermore, genetic studies in worm, y, and mouse havelinked enhanced stress resistance or reduced free radical" + ], + "answer": "Oxidative stress contributes to cellular aging through the accumulation of oxidative damage in various macromolecules, which leads to a decline in cellular function. This process occurs due to an imbalance between prooxidants and antioxidants, resulting in a steady-state accumulation of oxidative damage that increases with age [1]. The oxidative stress theory of aging posits that damage caused by reactive oxygen species (ROS) plays a critical role in determining lifespan, as it leads to the deterioration of biochemical and physiological processes [4]. Oxidative damage affects all cellular macromolecules, including lipids, proteins, and DNA, and this damage increases with age [3]. The accumulation of such damage is a key hallmark of aging physiology [5]. Specifically, oxidative damage to mitochondrial DNA (mtDNA) and the generation of ROS from the mitochondrial electron transport chain are significant contributors to this process [6]. Overall, the accumulation of oxidative damage is causally linked to aging and death, as it impairs cellular processes and bioenergetics, leading to the progressive loss of functional efficiency in cells [2], [8].", + "question": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_17 new file mode 100644 index 0000000..e34bfc3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Mechanisms of Vascular Aging.pdf", + "2017 - The Aging Cardiovascular System.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2022 - Proteomic analysis reveals that aging rabbit.pdf", + "2022 - Proteomic analysis reveals that aging rabbit.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2016 - The genome-wide role of HSF-1.pdf", + "2019 - Downregulation of miR-542-3p promotes.pdf", + "2007 - Sex-specific regulation of gene expression in the aging monkey aorta.pdf" + ], + "extraction_id": [ + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "d60f1e7d-cde2-5c66-8863-507065ed5c7f", + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "a099ce3c-cdff-5971-b3d5-f31e03aace96", + "c738a4b2-0aea-5157-bed4-fecdac9863b9", + "e91c9a2a-a797-59d5-8565-91b45b0113a1", + "b2c1c466-d4b3-5c01-a8a4-2f49e9f246a2", + "32322971-f8f4-53d3-8104-ac44cf03ebef", + "1d889462-37d6-5cb5-b0df-8ae9c50560b7" + ], + "document_id": [ + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "f6c524a5-acf9-5a07-8bbf-31091443cab3", + "f6c524a5-acf9-5a07-8bbf-31091443cab3", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "e3c48474-21da-51d2-b378-200138fda0d3", + "527e562f-f7c3-5a01-b70b-5737d63e2457", + "6c2a7135-31ed-57e3-89fa-42856979ea1a" + ], + "id": [ + "chatcmpl-AIHYGBcI0VJ8rQxINM8Z5Fqy6gz6y", + "9f768c0d-8518-5ac9-9d66-9ffdba704a84", + "e7f8f5f2-9102-56bf-b579-43ad3c8d6b84", + "b7cd7044-b2fe-5dd2-b7b4-6388b9f4765d", + "ab8d8d0e-f91a-538a-bd84-beafa1fe8ce8", + "e7121d85-7538-5cdd-8b2d-6d3d536439b9", + "cf5f0034-c806-52d6-bd26-137fb9d8a418", + "58e94400-b0f0-5757-b964-83a6b2b6f98f", + "4dfd7818-9111-5bf9-bbcf-e917b1c9b9fc", + "d5cd4d54-b051-5638-ba76-39c385f3e423", + "479ae037-3dd5-57f7-9bf7-78a3a45ac47f" + ], + "contexts": [ + "208 Additional features that contribute to increased ar - terial stiffness include decreased elastin synthesis, elastin degradation and fragmentation, elastin calcification, al-terations in cross-linking of extracellular matrix compo-nents (eg, by increased presence of advanced glycation end products). 208,210,211 The pathophysiological consequences of age-related ECM remodeling and arterial stiffening have been the sub-ject of a recent comprehensive review by AlGhatrif and Lakatta.", + "collagen. AGE-mediated cross-links can confer resis-tance to enzymatic degradation, and thus interferewith collagenolysis (56). In addition, increased ac- tivity of TGF- bwith aging stimulates the synthesis of interstitial collagen by vascular smooth muscle cells(VSMCs), and thereby augments arterial stiffness (57). Likewise, increased activity of the RAAS may augment collagen synthesis and heighten elastolysis (58). Endothelial dysfunction and arterial stiffness are", + "that many of these age-related ECM alterations are governed by circulating factors and factors produced in the vascular wall, including the extended renin-angiotensin-aldosterone system (see above) and an age-related decline in circulating IGF-1. 209 Collagen synthesis is also dysregulated with age in the vascular wall likely because of the effects of increased para-crine action of TGF- (transforming growth factor- ), 123 which contributes to vascular fibrosis and arterial stiffen-ing.", + "Ungvari et al Mechanisms of Vascular Aging 859 Role of Extracellular Matrix Remodeling in Vascular Aging The extracellular matrix (ECM) is an important contribu- tor to health and longevity. This noncellular compartment, ubiquitous to all tissues and organs does not only provide es-sential mechanical scaffolding but mediates highly dynamic biomechanical and biochemical signals required for tissue homeostasis, morphogenesis, and cell differentiation. Studies", + "1996;25(3):20915. 79. Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in development and disease. Nat Rev Mol Cell Biol. 2014;15(12):786801. 80. Swift J, Ivanovska IL, Buxboim A, Harada T, Dingal PCDP , Pinter J, et al. Nuclear Lamin-A scales with tissue stiffness and enhances matrix- directed differentiation. Science. 2013;341(6149):1240104. 81. Vogel C, Marcotte EM. Insights into the regulation of protein abun- dance from proteomic and transcriptomic analyses. Nat Rev Genet.", + "result in extracellular matrix stiffness in aging larynx and other organs [59, 79]. Finally, Lamin A was upregulated by dehydration, by a smaller magnitude, especially when observing the mean difference within the young groups. Previous data has identified that Lamin proteins A and C are important for imparting the nucleus with its stiff - ness, and their expression has been reported to scale with", + "aging. Annu Rev Biomed Eng. 2015;17:113141. doi: 10.1146/ annurev-bioeng-071114-040829 208. Jacob MP. Extracellular matrix remodeling and matrix metalloprotein- ases in the vascular wall during aging and in pathological conditions. Biomed Pharmacother. 2003;57:195202. 209. Tarantini S, Valcarcel-Ares NM, Yabluchanskiy A, Springo Z, Fulop GA, Ashpole N, Gautam T, Giles CB, Wren JD, Sonntag WE, Csiszar A, Ungvari Z. Insulin-like growth factor 1 deficiency exacerbates hyperten-", + "able human diseases such as osteoporosis and musculo- skeletal diseases [53]. Collagens are long-lived proteins known to accumulate damage during aging, leading to a decline in tissue health [54]. Also, type I collagens be- come resistant to proteolysis upon age [55, 56], affecting their turnover. Interestingly, mice expressing cleavage- resistant type I collagen go through an accelerated aging process [57]. Thus, cellular aging can be affected by the state of the extracellular matrix in mammals.", + "the characteristics of endothelial dysfunction and pheno- typic transition of smooth muscle cells, resulting in in- creased vascular stiffness and increased thickness of vascular walls. It has been reported that the age- associated phenotypic transition of VSMCs is a crucial contributor to vascular remodeling [ 17,25]. However, the mechanism that drives phenotypic transition ofVSMCs with aging remains unclarified. In this study, using RNAs extracted from the in vitro cultured VSMCs,", + "downregulation with aging of genes involved in the synthesisof the ECM and in particular of different forms of collagen(Table 2). In addition, aging males but not females showed adecrease in collagen type III. Interestingly, collagen type IIIdecreases the size of collagen bundles and thereby increasesvascular elasticity (11). Therefore, a decreased expression ofcollagen type III can participate in the increased stiffness thatcharacterizes the aging aorta (23). An interesting observationfrom our study that" + ], + "answer": "Extracellular matrix (ECM) remodeling and tissue stiffness are significant factors in the aging process. As we age, several changes occur in the ECM that contribute to increased tissue stiffness. These changes include decreased elastin synthesis, elastin degradation and fragmentation, and alterations in the cross-linking of ECM components, such as increased presence of advanced glycation end products (AGEs) [1]. AGEs can interfere with collagenolysis by forming cross-links that confer resistance to enzymatic degradation, thereby contributing to increased arterial stiffness [2]. Additionally, the activity of transforming growth factor-beta (TGF-\u03b2) increases with age, stimulating the synthesis of interstitial collagen by vascular smooth muscle cells (VSMCs), which further augments arterial stiffness [2]. The renin-angiotensin-aldosterone system (RAAS) also plays a role in this process by augmenting collagen synthesis and promoting elastolysis [2]. The ECM is crucial for providing mechanical scaffolding and mediating biomechanical and biochemical signals necessary for tissue homeostasis and cell differentiation [4]. However, with aging, ECM stiffness increases, affecting various organs, including the larynx [6]. This increased stiffness is associated with a decline in tissue health, as seen with the accumulation of damage in long-lived proteins like collagens, which become resistant to proteolysis and affect their turnover [8]. Overall, these changes in ECM remodeling and tissue stiffness contribute to the aging process by affecting vascular and tissue elasticity, leading to conditions such as arterial stiffening and vascular remodeling [1], [3], [9].", + "question": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_18 new file mode 100644 index 0000000..157f9ff --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Epigenetics and aging.pdf", + "2012 - Genome-Wide RNAi Longevity Screens in Caenorhabditis elegans.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2016 - Epigenetics and aging.pdf", + "2015 - The mechanism of ageing primary role of transposable elements.pdf", + "2012 - Replicative and Chronological Aging.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf" + ], + "extraction_id": [ + "9b7b806c-cac3-549e-9ae9-424cc3e5f869", + "f160f818-03bf-5b4e-b1f4-bfbd3b0bfb99", + "a972e2fb-b73f-51bf-980a-85c9db1482be", + "a972e2fb-b73f-51bf-980a-85c9db1482be", + "9b7b806c-cac3-549e-9ae9-424cc3e5f869", + "20245b79-fa8f-52fc-832e-1478a080d6e1", + "8bc194af-6e9d-51c5-8116-6d4186a885dd", + "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", + "0d4cd402-e032-5edd-98eb-170357a3092e", + "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5" + ], + "document_id": [ + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "7589fec2-e893-5a4d-9f0c-09abb35858ab", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "de558db9-dc04-5bbd-83bf-3e3368ff906b", + "496e387e-4278-5f74-8ecc-4edc1cee7dfe", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6" + ], + "id": [ + "chatcmpl-AIHYO1XLJbUnaqsOWyFh9a97rwIzB", + "603183d9-d22c-5008-bfa5-147ee5df4198", + "a6d18c4e-632c-52a2-b3f9-6296025e0ce7", + "d43449f1-2d90-5e0e-8ba8-8afdc306f32d", + "ca8ae9e1-f598-56b9-952e-bb5bea62d8fe", + "581ca468-d3f3-5846-9fba-7f1f860df956", + "a2effd64-3d9d-5bdf-8fc6-0cd72762763d", + "f82ef429-c823-5173-a93b-5c476df110f5", + "949f7420-bfb6-564d-8537-18c47e40bbc6", + "8ede28e5-ed8e-5c68-bd03-18c3c96bb31b", + "82060e66-87b7-5ac2-9877-fc7b26325b73" + ], + "contexts": [ + "D. Carmona-Gutierrez, C. Ruckenstuhl, J. Ring, W. Reichelt, K. Schimmel, T. Leeb,C. Moser, S. Schatz, L.-P. Kamolz, C. Magnes, F. Sinner, S. Sedej, K.-U. Frhlich,G. Juhasz, T. R. Pieber, J. Dengjel, S. J. Sigrist, G. Kroemer, F. Madeo, Nucleocytosolic de-pletion of the energy metabolite acetyl-coenzyme a stimulates autophagy and prolongs lifespan. Cell Metab. 19, 431 444 (2014). 225. S. Gelino, M. Hansen, Autophagy An emerging anti-aging mechanism. J. Clin. Exp. Pathol. (Suppl. 4), pii: 006 (2012).", + "[73] Vellai, T. Autophagy genes and ageing . Cell Death Differ. , 2009 , 16(1), 94-102. [74] Kaeberlein, M.; Kapahi, P. Cell signaling. Aging is RSKy business . Science , 2009 , 326(5949), 55-6. [75] Hansen, M.; Chandra, A.; Mitic, L.L.; Onken, B.; Driscoll, M.; Kenyon, C. A role for autophagy genes in the extension of lifespan by dietary restriction in C. elegans. PLoS Genet. , 2008 . [76] Hansen, M.; Taubert, S.; Crawford, D.; Libina, N.; Lee, S.J.;", + "chinery and upstream regulators provide evidence for a transcriptional decline in autophagy gene expression with age in human monocytes. The identification of key genes contributing to a decline in autophagy are of great interest, as pharmacologic activation of au- tophagy has been linked with increasing lifespan in animal models, including mice [45]. Further, dysfunc- tional autophagy is now widely implicated in patho- physiological processes of many age-related diseases", + "invasive pathogens, and to transport these cargos to the lysosomes for degradation [25]. In the aging field, im- paired autophagy is considered one of the principal de- terminants of cellular aging, which is supported by in vitro and animal study findings that autophagy de- clines with age [26]. However, studies of autophagy and age in humans are sparse. One of the most significant age-gene expression asso- ciations we observed in monocytes from 1,264 individ-", + "226. F. Madeo, N. Tavernarakis, G. Kroemer, Can autophagy promote longevity? Nat. Cell Biol. 12, 842 846 (2010). 227. J. Fllgrabe, M. A. Lynch-Day, N. Heldring, W. Li, R. B. Struijk, Q. Ma, O. Hermanson, M. G. Rosenfeld, D. J. Klionsky, B. Joseph, The histone H4 lysine 16 acetyltransferase hMOF regulates the outcome of autophagy. Nature 500, 468 471 (2013). 228. F. Ng, B. L. Tang, Sirtuins modulation of autophagy. J. Cell. Physiol. 228, 2262 2270 (2013).", + "(2013) The hallmarks of aging. Cell 153(6):11941217. doi: 10. 1016/j.cell.2013.05.039 3. Vellai T, Takacs-Vellai K, Sass M, Klionsky DJ (2009) The regulation of aging: does autophagy underlie longevity? TrendsCell Biol 19(10):487494. doi: 10.1016/j.tcb.2009.07.007 4. Kirkwood TB (2008) A systematic look at an old problem. Nature 451(7179):644647. doi: 10.1038/451644a 5. Koubova J, Guarente L (2003) How does calorie restriction work? Genes Dev 17(3):313321. doi: 10.1101/gad.1052903", + "Eisenberg, T., Knauer, H., Schauer, A., Bu ttner, S., Ruckenstuhl, C., Carmona- Gutierrez, D., Ring, J., Schroeder, S., Magnes, C., Antonacci, L., et al. (2009).Induction of autophagy by spermidine promotes longevity. Nat. Cell Biol. 11, 13051314. Enns, L.C., Morton, J.F., Treuting, P.R., Emond, M.J., Wolf, N.S., Dai, D.F., McKnight, G.S., Rabinovitch, P.S., and Ladiges, W.C. (2009). Disruption of protein kinase A in mice enhances healthy aging. PLoS ONE 4, e5963.", + "its essential part in the anti-aging mechanism of caloric restriction. Ann N Y Acad Sci. 2007;1114:69 78. 41. Cuervo AM, Bergamini E, Brunk UT, Droge W, Ffrench M, Terman A. Autophagy and aging: the importance of maintaining clean cells. Autophagy. 2005;1:131 40. 42. Terman A. The effect of age on formation and elimination of autophagic vacuoles in mouse hepatocytes. Gerontology. 1995;41 Suppl 2:319 26. 43. Donati A, Recchia G, Cavallini G, Bergamini E. Effect of aging and anti-aging", + "103 Experimental findings showing increased oxidative stress, impaired bioavailability of NO, and upregulation of in-flammatory mediators in autophagy-deficient endothelial cells support this view. 104 Further, pharmacological interventions that stimulate autophagy (eg, trehalose or spermidine treat-ment) were reported to reverse aspects of arterial aging. 105,106 Proteasomes degrade unneeded or damaged proteins by pro-teolysis. There is evidence that proteasome activity declines in advanced aging", + "Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy. Science. 2011;331:456 61. 38. Xiao B, Sanders MJ, Underwood E, Heath R, Mayer FV, Carmena D, et al. Structure of mammalian AMPK and its regulation by ADP. Nature. 2011;472:230 3. 39. Tang D, Kang R, Livesey KM, Cheh CW, Farkas A, Loughran P, et al. Endogenous HMGB1 regulates autophagy. J Cell Biol. 2010;190:881 92. 40. Bergamini E, Cavallini G, Donati A, Gori Z. The role of autophagy in aging:" + ], + "answer": "Recent developments in autophagy research have significantly enhanced our understanding of its role in aging and longevity. Here are some key contributions: 1. **Energy Metabolism and Lifespan Extension**: Research has shown that the depletion of acetyl-coenzyme A, an energy metabolite, can stimulate autophagy and prolong lifespan. This suggests that autophagy is closely linked to energy metabolism and can be a mechanism for extending lifespan [1]. 2. **Autophagy as an Anti-Aging Mechanism**: Autophagy is increasingly recognized as an emerging anti-aging mechanism. It plays a crucial role in maintaining cellular homeostasis by degrading and recycling damaged cellular components, which is essential for longevity [1]. 3. **Genetic Regulation and Dietary Restriction**: Studies have identified autophagy genes as important for lifespan extension, particularly in the context of dietary restriction. This indicates that genetic regulation of autophagy is a key factor in promoting longevity [2]. 4. **Pharmacological Activation**: There is evidence that pharmacological activation of autophagy can increase lifespan in animal models, including mice. This highlights the potential for therapeutic interventions targeting autophagy to promote healthy aging [3]. 5. **Impaired Autophagy and Cellular Aging**: Impaired autophagy is considered a principal determinant of cellular aging. The decline in autophagy with age is linked to various age-related diseases, emphasizing the importance of maintaining autophagic activity for longevity [4]. 6. **Spermidine and Longevity**: The induction of autophagy by compounds like spermidine has been shown to promote longevity, further supporting the role of autophagy in extending lifespan [7]. These findings collectively underscore the critical role of autophagy in aging and longevity, suggesting that enhancing autophagic processes could be a viable strategy for promoting healthy aging and extending lifespan.", + "question": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_19 new file mode 100644 index 0000000..82e1f47 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "2011 - Genome-wide promoter DNA methylation dynamics of human hematopoietic.pdf", + "2009 - Aging and Replicative Senescence Have Related Effects.pdf", + "2010 - Age-related molecular genetic changes of murine.pdf", + "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "2009 - Aging and Replicative Senescence Have Related Effects.pdf", + "2013 - Age-associated epigenetic drift implications.pdf", + "2007 - Two faces of p53 aging and tumor suppression.pdf", + "2013 - Effects_of_age_and_strain_on_cell_prolif.pdf", + "2010 - Age-related molecular genetic changes of murine.pdf" + ], + "extraction_id": [ + "fca849bb-6e08-5200-8c66-5250e902dca3", + "3be2a7fa-1d97-5280-ba37-cc3d311cfb75", + "f5b29cc7-fe8b-5230-adb1-0531fb1c3187", + "d39327b0-59b1-5e24-813d-099a48a8de85", + "188bdad0-f63b-5e4c-8eed-73cd01b8d66f", + "23921b67-8911-5086-a2e4-a909394a6df4", + "24500f0a-0e60-574e-9039-e9dd3b5be569", + "270c5516-f5b2-54d3-8865-b84d8a9506c1", + "b0fb2185-a2ee-5174-94d0-877ad2d87158", + "d39327b0-59b1-5e24-813d-099a48a8de85" + ], + "document_id": [ + "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "30081f4e-7189-5c9f-abf2-895250c0173e", + "0703ba80-b7a5-5873-9ab0-5d66d57f4750", + "a69ce6db-4a5e-58a5-9dc5-d529768edcb1", + "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "0703ba80-b7a5-5873-9ab0-5d66d57f4750", + "8513121f-71f3-5bb0-9433-feece9fd9fbc", + "b1ef905a-c145-5270-9110-ae6954ea3d72", + "d7e861e7-cdee-5145-9403-ef05e2d532c0", + "a69ce6db-4a5e-58a5-9dc5-d529768edcb1" + ], + "id": [ + "chatcmpl-AIHYWWczI6kl71Lbbg4Wx4xLfOmE6", + "cade861a-f60d-51fd-bfac-edce8860b395", + "7fcd630b-0f09-5947-8a28-f72d4418d8f8", + "8f53ce05-7527-52f2-8a25-9c3ee9a38861", + "ccf7dace-b7d8-576f-bb59-c6707e5180f5", + "f8e0e878-451b-519d-b6e5-e9834d5d3b77", + "de67cf90-712a-5c28-9f6b-404d84a06d22", + "e6bb4c40-7fe8-5ff7-af36-1c2b749ed1fb", + "01740a78-e141-56f0-8f34-7c02c5602344", + "ae2ad88f-6e02-5541-b6be-966fef7712f1", + "1dffbbdb-f76d-581b-8384-751ce5f41e90" + ], + "contexts": [ + "into old versus young recipients (Liang et al., 2005 ). Further experiments demonstrated that the muscle stem cell niche adversely effects stem cell function as evidenced by the restoration of old stem cell regenerative potential upon expos ure to a young systemic microenvironment (Conboy et al., 2005; Conboy and Rando, 2005). It has also been reported that the spermatogoni al stem cell niche deteriorates with age, causing the failure to suppor t an appropriate balance between stem cell self-renewal and", + "matopoietic stem cells is regulated by the stemcell niche. Exp Gerontol. 2008;43(11):974-980. 18. Geiger H, Rudolph KL. Aging in the lympho- hematopoietic stem cell compartment. Trends Immunol. 2009;30(7):360-365. 19. Muller-Sieburg C, Sieburg HB. Stem cell aging: survival of the laziest? Cell Cycle. 2008;7(24): 3798-3804. 20. Beerman I, Maloney WJ, Weissmann IL, Rossi DJ. Stem cells and the aging hematopoieticsystem. Curr Opin Immunol. 2010;22(4):500-506. 21. Teschendorff AE, Menon U, Gentry-Maharaj A,", + "Abstract The regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells. Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation. This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might", + "Because of their plasticity and accessibility these cells are also prime candidates for regenerative medicine. The contribution of stem cell aging to organismal aging is un der debate and one theory is that reparative processes deteriorate as a consequence of stem cell aging and/or de crease in number. Age has been linked with changes in osteogenic and adipogen ic potential of MSCs. Results: Here we report on changes in global gene expression of cultured MSCs isolated from the bone marrow of", + "suggesting that stem cells are not likely to be a factor limiting hematopoietic regeneration with age. However, their func-tional decits do show that HSCs are impacted by the forces of aging in a manner similar to that of differentiated cells [3134]. In our molecular analysis, we identied global age-related changes in gene expression in murine HSCs, with a view to identifying mechanisms that could be responsible for these age-associated declines in HSC function. Genes involved in", + "Discussion The deterioration of the regenerative potential upon aging might be due to functional changes in adult stem cells. To test this hypothesis we have investigated differential gene expression in primary, human MSC and HPC derived from different agegroups. In this study, we demonstrate for the first time age-related gene expression changes in human MSC and HPC and that there", + "cells, which may explain the observed decline of stem cell function with age. Age-associated increases inDNAm target developmental genes, overlapping those associated with environmental disease risk factors and with disease itself, notably cancer. In particular, cancers and precursor cancer lesions exhibit aggravated", + "tion associated with age: loss of stem cell pool division potential (loss of regenerative capacity) and loss ofdierentiated somatic cell function, which directly leads to loss of organ function. Loss of dierentiated somatic cell function can additionally indirectly aect adult stem and progenitor cells by altering the tissue microenviron- ment that is essential for stem cell support (the stem cellniche). In general, loss of stem cell pool division potential", + "1. Introduction Stem cell aging is regarded as one of the contributors to several degenerative conditions af icting the elderly because it underlies the physiological decline in tissue maintenance and regenerative capacity of many organs ( Rossi et al., 2008 ). The brain is one such organ that contains discrete populations of stem cells and their precursors (collectively referred to as neural progenitor cells [NPCs]) that continue to generate new neurons throughout life", + "spective of tissue regeneration and repair because there isevidence that these beneficial functions may becomehandicapped with age. Age-related decline in the numberof MSCs in the bone marrows of rodents, monkeys, andhumans have been reported [26-33]. Most studies to datefocused on the effects of aging on the ability of MSCs toenter osteogenic, chondrogenic and adipogenic pro-grams. Some, but not all studies suggest that agingreduces osteogenesis and chondrogenesis while enhanc-" + ], + "answer": "Age-related shifts in stem cell niche composition and function have significant implications for tissue regeneration capacity. As individuals age, the stem cell niche, which is crucial for maintaining stem cell function, undergoes changes that can adversely affect the regenerative potential of stem cells. Here are some key implications based on the provided context: 1. **Deterioration of the Stem Cell Niche**: The aging process leads to the deterioration of the stem cell niche, which can result in a failure to support the balance between stem cell self-renewal and differentiation. This is evident in the spermatogonial stem cell niche, which deteriorates with age, affecting its ability to maintain stem cell function [1]. 2. **Functional Impairments of Stem Cells**: The regenerative potential diminishes with age due to functional impairments in adult stem cells. This is linked to the phenomenon of replicative senescence, where cells lose their ability to proliferate after a certain number of divisions [3]. 3. **Changes in Gene Expression**: Age-related changes in gene expression have been observed in stem cells, such as mesenchymal stem cells (MSCs) and hematopoietic progenitor cells (HPCs). These changes can lead to declines in stem cell function and, consequently, a reduction in tissue regeneration capacity [6]. 4. **Loss of Stem Cell Pool Division Potential**: Aging is associated with a loss of stem cell pool division potential, which directly impacts the regenerative capacity of tissues. This loss can also indirectly affect adult stem and progenitor cells by altering the tissue microenvironment essential for stem cell support [8]. 5. **Reduction in Stem Cell Numbers**: There is evidence of a decline in the number of MSCs in the bone marrow with age, which can further hinder the ability of these cells to participate in tissue regeneration processes such as osteogenesis and chondrogenesis [10]. Overall, these age-related shifts in stem cell niche composition and function contribute to a decline in the body's ability to repair and regenerate tissues, which is a hallmark of aging and is linked to various degenerative conditions [9].", + "question": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_2 new file mode 100644 index 0000000..13e3ffc --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Nuclear Genomic Instability.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2018 - Nuclear Genomic Instability.pdf", + "2019 - Integration of heterogeneous functional.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Genome Integrity in Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "2022 - Functional genomics of inflamm-aging.pdf" + ], + "extraction_id": [ + "4b00515d-e599-5ce1-84e3-012d7efe1a30", + "eebc478a-d4b4-5547-a7e0-9c305d8bbd0f", + "fe836e95-1d70-51e5-b3fe-2f3005517606", + "c21052ac-b3d9-59bc-8164-3d2df613929f", + "b1eabac8-e6d1-50ba-9c42-60c107b56a65", + "97753738-7225-59cc-b573-72cdf4ba569d", + "3625a61e-f376-5bea-b2c9-582b6ef16957", + "6a2a94de-cfc0-50eb-b50e-bf3a0f813c78", + "ea17d9f1-0991-5a69-930d-3212a3fabe1f", + "72b29fff-be72-5ede-85c9-7dc81894c956" + ], + "document_id": [ + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "62b635c3-040e-512a-b016-6ef295308a1e", + "54d28a91-8db6-56b1-baaa-b67274c93a36", + "cf134202-50af-5700-9b1b-962501d9470d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "435dc081-e3d1-52c5-93a1-caa11206422f" + ], + "id": [ + "chatcmpl-AIHWHar7CqMtwymwPwmCVtJ5SKWUm", + "4d256f76-7065-5eeb-a961-db7e7cbe75ff", + "92618cf9-f512-5011-9d76-17f313ad850e", + "77589e08-f16b-5bb2-9f89-833f1833d5be", + "e1dde75e-c4f2-51f6-b601-abe56c2109c3", + "8c9d1720-5a2d-5559-831d-419208813d61", + "955cffc5-cb1d-5638-bb3e-bbf5b0fe5dd4", + "28976d8b-7996-51e7-b35b-213476f6ed7b", + "6d4a1a0b-2af3-5cc4-b7c0-a7223ce3edfa", + "a7675e04-876d-5026-88b5-842cd4ca237a", + "da4ca7c3-653d-584f-8956-7f3f710fd45e" + ], + "contexts": [ + "SASP (senescence-associated secretoryphenotype):cytokines, chemokines,proteases, and otherfactors secreted bysenescent cells, whichare inammatory anddisrupt tissuehomeostasis viaparacrine mechanisms ATM (ataxia-telangiectasiamutated):serine/threoninekinase and centralregulator of the DDR;activated by DNAdamage and transducesthat signal througheffectorphosphorylationphenotype (SASP) (84). SASP proteins include interleukin-6 (IL-6), transforming growth factor-", + "SASP is one of the most representative features of senescent cells and may explain the organismal expression of aging and age-related diseases. Senescent cells pro- duce a deleterious microenvironment through the production and secretion of pro- liferative and proinflammatory molecules such as IL-1 and -1, IL-6, IL-8, the chemotactic cytokine GRO, IGBP-7, growth factors, VEGF, TGF-, serine prote- ases, and matrix remodeling enzymes [146]. It has been determined that the activa-", + "context. For example, SASP likely contributes to early tumorigenesis (84), chemoresistance (94),and potentially neurodegenerative diseases (95). However, SASP is also important for mammalian development (96), tissue repair (97), and wound healing (98). SASP plays an important role in stimulating clearance of damaged, senescent cells by the innate immune system (99). However,inefcient immune clearance of senescent cells in aged organisms is thought to contribute to chronic inammation of aging.", + "many tissues, where theSASP promotes chronic inflammation and exacerbates age-associated degeneration and hyperplasia. Recent evidence suggests that neurological aging and neurode- generation areaccompanied byanaccumulation ofsecretory cells inbrain, suggesting that cel- lular senescence may contribute tobrain aging [2]through ashared mechanism. Overlapping mechanisms canbedetected using functional genomics studies ofboth thebiology ofcellular senescence and cognitive aging.", + "senescence-associated with the secretory phenotype (SASP) are other markers of cellular senescence. Inflammation andIntercellular Communication While senescent cells no longer replicate, they are still metabolically active and secrete proteins in a recognizable pattern known as SASP.This is a widely heteroge- neous group of proteins with autocrine and paracrine effects [47], including soluble signaling factors, such as interleukins, chemokines, and growth factors, as well as", + "matory mediators. This particular phenotype is termed the senescence- associated secretory phenotype (SASP). Replicative cellular aging includes biochemical, mor - phological, and functional modifications that lead to the irreversible impairment of cell proliferation associated with DNA damage, shortening of the telomeres, and changes in chromatin architecture, as previously described [135, 136]. The molecular mechanisms that drive cellular senescence in proliferative and", + "secretion of a range of proinammatory cyto- and chemokines, a state that has been dened asthe senescence-associated secretory phenotype (SASP) (103). Major SASP factors include IL1, IL6, IL8, and various matrix metalloproteases (MMPs), all of which individually are thought to drive aging and age-related diseases. Thus, DNA damage is a major determinant in controllingcell death, stem cell exhaustion, and cellular senescence, which are considered important events", + "senescent cells [150]. SASP factors exert their functions in either an autocrine or a paracrine manner and are responsible for the induction of the chronic inflammation and cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu- mulation of senescent cells in tissue is closely associated with aging-related dis- eases. Recently, it was determined that senescent fibroblasts significantly increase the expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "Role of L1 and Alu in cellular senescence and age-related inflammation A key feature of cellular senescence is the senescence-associatedsecretory phenotype (SASP), whereby senescent cells secretenumerous proinflammatory cytokines, chemokines, growth factors, and proteases (Campisi, 2013). This altered secretome", + "8. Coppe JP, Patil CK, Rodier F, et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous func- tions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol2008; 6:285368. 9. Wiley CD, Liu S, Limbad C, et al. SILAC analysis reveals increased secretion of hemostasis-related factors by senes- cent cells. Cell Rep 2019; 28:33293337 e3325. 10. Basisty N, Kale A, Jeon OH, et al. A proteomic atlas of senescence-associated secretomes for aging biomarker" + ], + "answer": "The latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction highlight several key aspects: 1. SASP factors, which include cytokines, chemokines, proteases, and other inflammatory molecules, are secreted by senescent cells and disrupt tissue homeostasis through paracrine mechanisms [1]. These factors contribute to a deleterious microenvironment that promotes aging and age-related diseases [2]. 2. SASP is associated with chronic inflammation and exacerbates age-associated degeneration and hyperplasia in many tissues [4]. This chronic inflammation is a significant contributor to tissue dysfunction as organisms age. 3. The accumulation of senescent cells and their SASP factors is closely linked to aging-related diseases. These factors can induce chronic inflammation and cell proliferation, leading to cell dysfunction and potentially cancer [8]. 4. SASP factors exert their effects in both autocrine and paracrine manners, influencing not only the senescent cells themselves but also the surrounding tissue environment [8]. Overall, SASP factors play a critical role in driving the chronic inflammation and tissue dysfunction associated with aging, highlighting their importance in the study of age-related diseases and potential therapeutic targets.", + "question": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_20 new file mode 100644 index 0000000..fecccf3 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes b.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2004 - Diabetes Genes a.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2007 - Rage gene promoter polymorphisms and diabetic retinopathy in a clinic-based population from South India.pdf", + "2004 - Diabetes Genes b.pdf", + "2004 - Diabetes Genes a.pdf", + "2004 - Diabetes Genes a.pdf" + ], + "extraction_id": [ + "a3427d8a-366e-5edc-9a9d-fa1da5d9e800", + "60ec7e90-7c38-5bda-a94e-ef15369c710c", + "272b3625-6f21-51f5-a83b-cfdbf4ddc841", + "cc350a5a-f474-597d-93c8-4359b9ddcc38", + "f5f2abef-9ccd-5147-a433-489c7225017c", + "98c7d4f6-45b7-53d4-979d-5503e91b1415", + "2903bc47-30d8-5e1c-acd9-5db4908f5ee9", + "26eeaac7-6846-51ee-a69b-51a75402a1bf", + "f6de03c3-cbbd-5963-ab23-e934f6ff1d56", + "60ec7e90-7c38-5bda-a94e-ef15369c710c" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "766edfd5-4756-51bf-b636-c94b041d030c", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "de5a5a08-3a63-587c-b835-41c74b37f570", + "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa" + ], + "id": [ + "chatcmpl-AIHYkQV1s8mGJ0u0OlIT1WoCFkj8X", + "388d90ef-1bfc-572d-b783-af945ab9519b", + "aad43b5f-c345-53c4-a37e-4b59e54082bb", + "edfb3091-1629-53bc-9f0b-88d552862fd9", + "3d613e0f-9ab0-575f-88cc-2b35f51f9d9d", + "34533770-24ba-57b7-95f9-06b201c92aa5", + "e1c2f05b-b04a-5c74-98ad-69af532d2ae9", + "50a3dd44-9747-5456-91e3-ebeb2b6a9248", + "a8fe389d-7249-50d5-8c4a-2f9d62fa73f6", + "94f15877-0b3a-5dee-8d1f-d0a034f14220", + "0b6eb47a-1fd1-58d2-81db-3a17b967f2d6" + ], + "contexts": [ + "vascular and kidney diseases [47]. Advanced glycation end-products (AGE) are the result of nonenzymatic glyca- tion, which produces heterogeneous bioactive molecules, such as lipids, proteins, and nucleic acids [59]. The accumulation of AGEs in aged tissues leads to several processes, such as inflammation, obesity, apoptosis, and other adverse processes related to ageing [47]. These AGEs are detected by various techniques, such as", + "and leading to vascular hypertrophy and stiffening of collagen with subsequent reduction of arterial compliance. These are processes that are associated with aging but seem to be accelerated by hyperglycemia. These cross-linked macromolecules, called advanced glycosylation end products (AGEs), are implicated in the pathogenesis of vascular complications. Once", + "proposed mechanisms are the development of advanced glycosylation end products and sorbitol accumulation. Advanced glycosylation end products (AGEs) comprise a heterogeneous group of molecules that accumulate in plasma and tissues with advancing age, diabetes and renal failure. They are characterized by browning, fluorescence, cross-linking and biological response through specific AGE receptors and were first described in 1912 by French chemist L.C. Maillard (Fig. 5).", + "the accumulation of AGEs which can further perp etuate and amplify local inflammation and 197 oxidant stress through irreversible glycation of the various protei ns and lipids to promote long 198 term vascular and end-organ damage. Thus AGEs, acting through receptors such as RAGE, 199 could also contribute to hyperglycemic memo ry (18, 96, 147). These studies have begun to 200", + "AGEs are taken up by specific AGE receptors (RAGE), cytokines, growth factors, and adhesion factors are released, leading to further cellular changes. AGEs also can impair endothelial function and vascular reactivity, such as in response to nitric oxide. Modification of LDL as a result of glycation may contribute to foam cell formation.4 Thus, AGEs appear to be main players not only in the development of diabetic complications and atherosclerosis,", + "geneous group of macromolecules that are formed by the nonenzymatic glycation of proteins, lipids, and nucleic acids. Overproduction of AGEs is considered the most important pathophysiological mechanism that induces diabetic complications (Semba etal. 2010). On one hand, AGEs mediate intracellular glycation of mitochondrial respiratory chain proteins and increase ROS levels, thus triggering oxidative stress (Coughlan etal. 2009) and endoplasmic reticulum stress (Piperi etal. 2012). On the", + "Introduction In individuals with diabetes, nonenzymatic glycation of proteins leads to the formation of advanced glycation end products (AGE) and this process occurs at an accelerated rate in chronic hyperglycaemia1, and also the levels are found to be increased in complications of diabetes, such as diabetic retinopathy (DR).2 AGE induces a variety of pathological changes, such as increased basement membrane thickening, arterial stiffness, and glomerular sclerosis.3,4AGEs bind to a specic receptor", + "AGEs accelerate atherosclerosis through cross-linking of proteins, platelet aggregation, defective vascular relaxation, and abnormal lipoprotein metabolism. 30 AGEs have a vital role in pathogenesis of diabetic nephropathy and progression of renal failure. Renal failure, in turn, results in decreased excretion and increased generation of AGEs (Figure 6). 629", + "vessels show enhanced subintimal protein and lipoprotein deposition; increased vascular permeability, e.g. to albumin; inactivation of nitric oxide; activation of endothelial receptors, leading to vasoconstriction and thrombosis; altered proteoglycan milieu; altered basement membrane cellular structure; proliferation of matrix. Strategies directed at the prevention of formation or the disruption of AGE cross-links may be promising. REFERENCES:", + "proteins and nucleic acids, leads to modification and then decline in structure and function of these molecules, as the cross-links accumulate both extracellularly and intracellularly over time. A prime example would be the crosslinking of collagen, which is thought to lead to typical phenomena observed in aging, such as increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, the formation of cataracts, and" + ], + "answer": "The accumulation of cross-links and advanced glycation end-products (AGEs) significantly impacts the structural integrity and function of aging tissues in several ways: 1. **Inflammation and Oxidative Stress**: AGEs accumulation leads to inflammation and oxidative stress, which can cause long-term vascular and end-organ damage [1], [4]. This is partly due to the interaction of AGEs with specific receptors such as RAGE, which perpetuates these adverse processes. 2. **Vascular Changes**: AGEs contribute to vascular hypertrophy, stiffening of collagen, and reduced arterial compliance, which are associated with aging and are accelerated by hyperglycemia [2]. This stiffening of collagen and reduction in arterial compliance can lead to decreased vascular function and increased risk of vascular complications. 3. **Cross-linking of Proteins**: AGEs cause cross-linking of proteins, which affects the structural integrity of tissues. For example, the cross-linking of collagen is associated with increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, and the formation of cataracts [10]. 4. **Endothelial Dysfunction**: AGEs impair endothelial function and vascular reactivity, which can lead to complications such as atherosclerosis and diabetic complications [5]. This impairment is due to the modification of lipoproteins and the release of cytokines and growth factors upon AGE interaction with receptors. 5. **Pathological Changes in Tissues**: AGEs induce various pathological changes, including increased basement membrane thickening, arterial stiffness, and glomerular sclerosis [7]. These changes contribute to the decline in tissue function and structure as they age. Overall, the accumulation of AGEs and the resulting cross-links compromise the structural integrity and function of tissues, contributing to the aging process and the development of age-related diseases.", + "question": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_3 new file mode 100644 index 0000000..a3f6cd5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - Remodeling of epigenome and transcriptome.pdf", + "2013 - Transposable elements become active and mobile in the genomes.pdf", + "2010 - Higher-order Genome Organization.pdf", + "2007 - The role of nuclear architecture.pdf", + "2010 - Higher-order Genome Organization.pdf", + "2007 - The role of nuclear architecture.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2010 - Higher-order Genome Organization.pdf", + "2016 - Epigenetic Mechanisms of Longevity and Aging.pdf", + "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf" + ], + "extraction_id": [ + "ab26a306-0581-5bdc-a6d1-689622689e90", + "dab38594-466b-50bc-8213-150f3862ff03", + "c4a47fc1-b528-5e29-9d13-e64be4e04938", + "c5185d6d-b244-57d7-886c-2ebb364a3ac7", + "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "b36b1865-2949-50be-ad95-bdc9d05b82eb", + "04e838ad-d90d-5e9d-af94-8e975af339a0", + "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "718d36c5-299d-596e-90be-416d12f7b5d1", + "6efb8add-cedc-5089-9374-2466867e388a" + ], + "document_id": [ + "87ffccee-fc33-5373-948d-67736aa0f069", + "c6901c06-c8ed-5220-a989-807bacdc9d0d", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "91339298-860e-57d0-b58d-5a4571b4fc2b", + "588185a0-e157-552f-a304-4beefb85d398", + "7412a162-ee3b-5f09-9886-8e9172dd3ee8" + ], + "id": [ + "chatcmpl-AIHWNXCXElapoM0J1wCt0Uh4pwpDs", + "1290eb6d-c454-5177-b55c-2e0f17265ab8", + "f51d2566-aef3-51af-ac47-cfba546bd293", + "212e1fcc-f0f0-5bd0-81af-aea694179b9e", + "12a416a1-9833-5e88-b86d-7ce6c54850b7", + "bada4b21-3c6d-55a4-b857-091a3a86f65d", + "ebd7a483-80a4-5f16-959d-e021635c88db", + "b2d6de59-f3d4-5f74-9bcb-96f00f885ba2", + "fa95b6a0-b4ef-5343-95aa-93d38aa291be", + "a681ba09-0707-5611-9a91-36f9967f91c8", + "14898b2f-4643-5362-be34-31d5ee5a4be6" + ], + "contexts": [ + "loss of chromatin homeostasis drives aspects of aging. As chroma-tin marks are relatively stable and can even persist through cell divi-sion (Kouskouti and Talianidis 2005), sustained alterations to thechromatin landscape may mediate the propagation of age-associat- ed functional decline. Age-dependent changes in chromatin marks (e.g., DNA meth- ylation, histone modifications) have been observed in multiple species and tissues (Benayoun et al. 2015; Booth and Brunet", + "contributes to the onset of tissue dysfunction and the eventual demise of organisms as they age. During replicative senescence of human fibroblasts chromatin is subject to extensive changes in the global distribution of euchromatin and heterochromatin [25,35]. We found that the fundamental architecture of the genome undergoes profound alterations: an overall closing of chromatin in euchromatic gene-rich regions, which is", + "impaired function of histone modifying activ-ities, which in turn lead to structural chroma- tin changes. The number of known diseasesOrganismal agingAging-associated gene expression programsCellular stress DNA damageChromatin remodelingEpigenetic status SusceptibilityHistone modifier redistribution Non-specific gene expression events Figure 3. Chromatin effects in aging. A complex network of interactions links chromatin structure to aging.", + "by Pelicci and colleagues in this issue). However, it could also be argued that chromatin structure is directly affected by the ageing process through an as-yet-unknown mecha - nism that leads to increased DNA damage and a perma - nent damage response that alters gene-expression patterns in a similar way to the model proposed in this review. o ver the coming years, as researchers use mammalian models to map the global pattern of chromatin modifi -", + "and peripheral heterochromatin blocks are lost during aging (Haithcock et al. 2005). The aging-associated defects in chromatin structure have various functional consequences.T o start with, aged genomes are characterized by increased DNA damage and high levels of per-sistent DNA breaks, possibly brought about by structural changes, which increase the suscepti- bility of the genome to damage. Furthermore,probably as a consequence of loss of pericentro- meric heterochromatin structure, physiologi-", + "related changes in gene expression and the ageing process4,5. Changes in gene expression were already known to contribute to cellular senescence6, a possible cause of ageing7, and may provide an explanation for the age-related decline in organ and tissue function in complex organisms.Although chromatin reorganization was linked to ageing in budding yeast over 10 years ago8,9, these ideas have remained untested. Recently, a growing appre - ciation for the importance of chromatin in regulating", + "tone loss in the ageing process has been attributed to alterations in heterochromatin, which are characterized by a decrease in its distribution in the genome and the content of characteristic heterochromatin histone marks (such as H3K9me3 and H3K27me3) as evidenced in fibroblasts cells from a HGS patient and healthy aged individuals [59, 60]. Interestingly, it has been suggested that the increase in chroma- tin opening in T cells from aged people could be related to histone loss, which in", + "long lifespan (Dang et al. 2009). Given theseextensive changes in histone modications, not surprisingly, aged cells show dramatic and global misregulation of gene expression. Al-though some of these changes are likely part of specic aging-related gene expression pro- grams including inammation and cellularstress responses, others likely occur largely sto- chastically because of random changes in epi- genetic modications and chromatin structure. The mechanisms that drive chromatin and", + "general loss of histones coupled with local and global chromatinremodeling, an imbalance of activating and repressive histone modications, and transcriptional change in all aging models. Additionally, particularly in mammalian systems, there is globaland local change in DNA methylation, site-specic loss and gain in heterochromatin, and signicant nuclear reorganization (Figure 1 ). It is as yet unclear whether changes in the activity of epigenetic", + "Amarcb1) as well as histone deacetylases (Hdac1, -5, and -6) and a DNA methyltransferace (Dnmt3b) were downregulated in aged cells. They also showed that several chromosomal regions changed with age in a coordinated manner resulting in an overall increase in transcriptional activity. They propos e that chromatin dysregulation and epigenetic changes drive the loss of cellular function and ultimately drive the aging process in HSCs. Consistent with these data, Polycomb proteins (transcriptional" + ], + "answer": "Age-related changes in chromatin architecture contribute to the decline in cellular function through several mechanisms: 1. **Loss of Chromatin Homeostasis**: Sustained alterations in the chromatin landscape, such as changes in DNA methylation and histone modifications, can mediate the propagation of age-associated functional decline [1]. These changes are relatively stable and can persist through cell division, affecting cellular function over time. 2. **Changes in Chromatin Distribution**: During aging, there is an extensive change in the global distribution of euchromatin and heterochromatin. Specifically, there is an overall closing of chromatin in euchromatic gene-rich regions, which contributes to tissue dysfunction and the eventual decline of cellular function [2]. 3. **Increased DNA Damage**: Aging-associated defects in chromatin structure lead to increased DNA damage and persistent DNA breaks. This is possibly due to structural changes that increase the genome's susceptibility to damage, further contributing to the decline in cellular function [5]. 4. **Histone Loss and Chromatin Remodeling**: There is a general loss of histones and chromatin remodeling, leading to an imbalance of activating and repressive histone modifications. This results in transcriptional changes that are observed in all aging models, contributing to the decline in cellular function [9]. 5. **Epigenetic Changes and Gene Expression**: Age-related chromatin dysregulation and epigenetic changes drive the loss of cellular function by altering gene expression patterns. These changes can lead to increased transcriptional activity in certain chromosomal regions, ultimately driving the aging process [10]. These changes in chromatin architecture collectively contribute to the decline in cellular function observed with aging.", + "question": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_4 new file mode 100644 index 0000000..d1e0938 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2013 - Age-associated epigenetic drift implications.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "2016 - Epigenetic drift in the aging genome a ten-year.pdf" + ], + "extraction_id": [ + "f244a68b-5127-5507-94a2-d2b8ca84f0ee", + "0e274732-b0df-53b8-999b-30b798af92e2", + "915ca931-d49d-5837-97fd-f06c145764d0", + "0e274732-b0df-53b8-999b-30b798af92e2", + "42343f61-f147-520b-bd14-0c2bf7b63262", + "617f523f-b892-5bfc-b99c-2e67a4cc185f", + "704a88b4-f49e-57cb-b572-1fa948b6065b", + "f244a68b-5127-5507-94a2-d2b8ca84f0ee", + "7f8f4ca0-9b27-55e3-a889-030af08dc84b", + "2f6d20f0-addc-51e8-979d-1aac7ac26694" + ], + "document_id": [ + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "8513121f-71f3-5bb0-9433-feece9fd9fbc", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "62b635c3-040e-512a-b016-6ef295308a1e", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "52f09ef3-4e4c-538f-909c-d28eb72d91f3" + ], + "id": [ + "chatcmpl-AIHWU7LIWS22cXcNTfkSGgjRTVQIK", + "b4eebcc5-781b-505b-a340-305b29285c66", + "78059a6b-4809-5d36-b961-6fcddbb06f2b", + "6baf63a6-fa5a-54e2-8290-af586a51243f", + "ef0f46ad-2e78-5666-b83d-36d2920b64ea", + "02361135-a01e-55f2-9efa-b7c465f2498b", + "82815a35-f43e-56fc-a254-92b03a278ab5", + "b5f6d630-dc24-50d7-af74-b3034cbb1055", + "8822b363-e906-5f83-a494-caad665c7af2", + "0e8901a7-c123-5e96-97fe-4d5cd85eb0c9", + "0aede05b-f0dd-595a-a11d-acac0970d25d" + ], + "contexts": [ + "experiments suggest that epigenetic features associated withaging can be reversed. In successfully reprogrammed iPSCs, the chromatin state of CDKN2A locus associated with aging is erased and restored to that of youthful cells ( Meissner, 2010 ). The requirement for proper epigenetic gene silencing for longevity has been observed in multiple model organisms, sug- gesting an evolutionarily conserved process ( Lin et al., 2000; Chen et al., 2005; Greer et al., 2010 ). The function of Polycomb", + "apparent rewinding of the aging clock without loss of differenti-ation. Formal demonstration will require clear epigenetic signa- tures of young and old cells and evidence that the aged cells have regained a youthful signature. It should be noted thatreprogramming of the epigenome to a youthful state in an aged cell has inherent risks and uncertainties. For example, the", + "et al., 2010 ). Clearly, inhibiting single signaling pathways (NF-k B and mTOR) is sufcient to restore some features of youthful cells, but the number of transcriptional regulatorsthat need to be modulated to result in full rejuvenation is unknown. Third, is the youthful state or the aged state domi- nant? It would be interesting to determine which epigeneticand transcriptional prole is more robust in experiments of fusion of young and old cells. Concluding Remarks", + "Rejuvenation: Is It Epigenetic Reprogramming?By analogy to the attainment of a pluripotent state by epigenetic reprogramming of a differentiated cell, is cellular rejuvenation byheterochronic parabiosis, NF- kB inhibition, or inhibition of mTOR signaling ( Figure 1 ) a form of epigenetic reprogramming from an aged state to a youthful state? If so, then these would be examples of an uncoupling of the differentiation program from the aging clock, with cells in each case manifesting an", + "with a healthy lifestyle may preserve a more intact epigenome and hence experi-ence longevity. Reprogramming of aged cells into iPSCs and regeneration of dif-ferentiated cells may provide a mechanism for epigenetic rejuvenation. In addition to epigenetic drift, telomere shortening has been associated with", + "tion through the lens of epigenetic reprogramming. By dening youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges. Introduction The inexorable tolls of aging are evident in almost all living beings. From the onset of reproductive maturity, organismalaging is generally characterized by a decline in fecundity, an increased susceptibility to disease and tissue dysfunction, and increased risk of mortality ( Kirkwood, 2005; Hayick, 2007; Kirk-", + "others (i.e. DNA methylation influences chromatin structures, histones PTMs). Several important conclusions emerge from the presented findings: there are at least two ways to reverse or inhibit senescence by epigenetic mechanisms, whereby a healthy life expectancy could be prolonged. The first way involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or cells derived from very aged patients or patients with progeroid syndromes, by which the", + "aging is at least in part, if not largely, a manifestation of epigeneticchanges, including those that may be secondary to genomicmutations, offers a theoretical construct for understanding the mechanisms of rejuvenation. If so, it should be possible to char- acterize young and old cells by specic transcriptional andepigenetic proles and states. Furthermore, the processes that underlie aging and rejuvenation should be identiable in terms", + "determinants of the aged state by genetically manipulatingspecic biochemical pathways. A recent example demonstratesthe power of transcriptional proling and bioinformatic analysis to reveal an aging signature that can be genetically engineered to reect a more youthful state ( Adler et al., 2007 ). In a compar- ison of old and young tissues from mice and humans, old tissues were found to express at signicantly higher levels a set of genes that contained sequences in their 5 0regulatory regions, indica-", + "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing attention. The potential reversibility of these epigenetic changes that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases. 8 This is especially important given the remarkable plasticityof aging. 9,10In the literature, age-associated epigenetic alter- ations have been identified by epigenome-wide association" + ], + "answer": "Studying the epigenetic reprogramming of aged cells to a more youthful state has provided several insights: 1. **Reversal of Aging-Associated Epigenetic Features**: Experiments have shown that epigenetic features associated with aging can be reversed. For instance, in successfully reprogrammed induced pluripotent stem cells (iPSCs), the chromatin state of the CDKN2A locus, which is associated with aging, is erased and restored to that of youthful cells [1]. 2. **Potential for Longevity**: Proper epigenetic gene silencing is required for longevity, as observed in multiple model organisms. This suggests that the process of epigenetic reprogramming might be evolutionarily conserved and could play a role in extending lifespan [1]. 3. **Rewinding the Aging Clock**: There is an apparent ability to rewind the aging clock without losing cellular differentiation. However, this requires clear epigenetic signatures of young and old cells and evidence that aged cells have regained a youthful signature [2]. 4. **Risks and Uncertainties**: While reprogramming the epigenome to a youthful state holds promise, it also carries inherent risks and uncertainties, highlighting the need for further research to understand the full implications and safety of such interventions [2]. 5. **Mechanisms of Rejuvenation**: The study of epigenetic reprogramming provides a framework for understanding the mechanisms of rejuvenation, suggesting that aging is at least partly a manifestation of epigenetic changes. This offers opportunities to alter the trajectory of age-related diseases [8], [10]. 6. **Prolonging Healthy Life Expectancy**: There are at least two ways to reverse or inhibit senescence through epigenetic mechanisms, which could prolong healthy life expectancy. One involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or derived from very aged patients [7]. These insights collectively suggest that epigenetic reprogramming holds significant potential for reversing aging processes and extending healthy lifespan, although further research is needed to fully understand and safely harness these capabilities.", + "question": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_5 new file mode 100644 index 0000000..98923a8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2011 - Mitochondrial genome deletions and minicircles.pdf", + "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2017 - Independent impacts of aging.pdf" + ], + "extraction_id": [ + "ef9463cd-cf21-527f-ae4a-3df211c78435", + "391985ac-70b7-57c9-97b2-940d8ebd2366", + "8a8e649d-6689-5d6d-91b6-157abfd8f990", + "5cbace8d-e538-5531-9311-ea9726ad2f15", + "385c192b-a416-5208-9615-20111ce782aa", + "7cf75da1-3c2a-5155-84dd-0dfe77d3fe41", + "c7041bbd-983f-5532-8b0e-cbd5f114a75f", + "c8db1d28-f6c2-5896-95ec-bb01159ba483", + "d226a80b-8a07-52ea-82b8-30adce468571", + "1f0b6363-a045-53aa-a124-4cf89e61fc26" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "c28cecbc-be20-54e2-afdd-afb8d25b1ab1", + "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "d1d0b9ce-f827-5dfb-8e39-d87a9ca52f6d" + ], + "id": [ + "chatcmpl-AIHWdEvFttNJ6ZbP6sReC3nxIXsfz", + "4206977e-23df-5307-8d8a-cb2ed7b33595", + "7853fd79-e251-5e3f-8b6f-7d1ebf8182bc", + "1436639f-3759-5172-9b13-b1dd9105420e", + "7095cdbb-852e-541e-884b-a9e67c2c790c", + "a1ea550b-8017-58c5-a80f-f22f4869f792", + "8ec531e8-2692-5995-8f1e-246406b9de04", + "f41af83b-dd40-5128-b051-2b0f26942786", + "1a9d5c26-f606-5cb5-98ee-4120de3fbd1a", + "e183f824-0ca8-58aa-a06e-110a3a94c2e9", + "39019881-9b6d-5111-87ea-71c413bdf4ff" + ], + "contexts": [ + "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. 2014;306:E58191. 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial function in aging. Mech Ageing Dev. 2010;131:45162. 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "mechanisms that lead to mitochondrial metabolism shifts in human aging are not completely understood, the literature reports that the failure in the mitochondrial metabolism of aged heart might be associated with mutations in the mtDNA.In this sense, the aged heart shows an increase over 15-fold on mtDNA mutations in com- parison to hearts from young people [101]. Mutations in genes that encode Polg-a, responsible for mtDNA repair machinery, cytochrome b, and several subunits of", + "22. Fleming JE, Miquel J, Cottrell SF, Yengoyan LS, Economos AC: Is cell aging caused by respiration-dependent injury to the mitochondrial genome?Gerontology 1982, 28:, 44-53. 23. Pak JW, Herbst A, Bua E, Gokey N, McKenzie D, Aiken JM: Mitochondrial DNA mutations as a fundamental mechanism in physiological declinesassociated with aging. Aging Cell 2003, 2:1-7. 24. Jacobs HT: The mitochondrial theory of aging: dead or alive. Aging Cell 2003, 2:11-17.", + "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging. Mol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028 Symer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G. and Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic instability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5 Szabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "limitations to study mitochondrial metabolism in human samples, in this section we briefly described the implications of mitochondrial metabolism for aging in the most studied and high energy demand human tissues, such as skeletal muscle, heart, and brain.Table 4.1 Main mitochondrial dynamics proteins that are altered in human tissues during the aging process Tissue/ organ Fission Fusion Biogenesis Mitophagy Refs Skeletal muscleIncreased fragmentation Decreased Drp1 proteinIncreased interconnected", + "96. Wei Y-H, Wu S-B, Ma Y-S, Lee H-C.Respiratory function decline and DNA mutation in mitochondria, oxidative stress and altered gene expression during aging. Chang Gung Med J. 2009;32:11332. 97. Kates AM, Herrero P, Dence C, Soto P, Srinivasan M, Delano DG, Ehsani A, Gropler RJ. Impact of aging on substrate metabolism by the human heart. J Am Coll Cardiol. 2003;41:2939. 98. Gmez LA, Monette JS, Chavez JD, Maier CS, Hagen TM.Supercomplexes of the mito-", + "phenotype, such as the Mitochondrial Free Radical Theory of Aging (MFRTA), and although these theories have been recently confronted, the role of mitochondria in the aging process is undeniable because of their versatile roles and implications for cellular function. MFRTA suggests that the oxidative damage of mtDNA is the key event disturbing the respiratory chain proteins to induce its dysfunction and increase ROS production in a vicious cycle [123]. However, alterations in mito-", + "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/ s12864-017-4287-0. 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema- topoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "78 p53, which regulate the catalytic subunits of ETC complexes [103]. Unfortunately, these data have only been observed in murine models of aging and require further verification in human samples. Mitochondrial Metabolism intheAged Brain In normal conditions, the brain consumes around 25% of the total body glucose via glycolysis and mitochondrial OxPhos [104]. So besides the mitochondrial dynam- ics dysfunctions described above, during aging there is also a decline in energy", + "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569. 20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G, Freyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA mutations aggravate ageing and can impair brain development. Nature. 2013;501(7467):412 5. 21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20(8):1653 9." + ], + "answer": "Alterations in the mitochondrial genome and bioenergetics significantly influence the aging process in humans through several mechanisms: 1. **Mitochondrial DNA Mutations**: As humans age, there is an increase in mitochondrial DNA (mtDNA) mutations. These mutations can lead to a decline in mitochondrial function, which is a fundamental mechanism in the physiological declines associated with aging [3]. Specifically, the aged heart shows a significant increase in mtDNA mutations compared to younger hearts, which may contribute to the failure in mitochondrial metabolism observed in aging [2]. 2. **Respiratory Function Decline**: Aging is associated with a decline in respiratory function and increased oxidative stress, which can lead to further DNA mutations and altered gene expression in mitochondria [6]. This decline in mitochondrial respiratory function is linked to the production of reactive oxygen species (ROS), which can damage mtDNA and exacerbate mitochondrial dysfunction [7]. 3. **Mitochondrial Dynamics**: Changes in mitochondrial dynamics, such as increased fragmentation and decreased fusion, are observed in aging tissues like skeletal muscle, heart, and brain. These alterations can impair mitochondrial biogenesis and mitophagy, leading to reduced energy production and increased cellular stress [5]. 4. **Bioenergetic Shifts**: The aging process involves shifts in mitochondrial metabolism, particularly in high-energy-demand tissues. For example, the brain experiences a decline in energy production due to mitochondrial dysfunction, which can affect cognitive function and overall brain health [9]. Overall, the accumulation of mtDNA mutations, decline in mitochondrial respiratory function, and alterations in mitochondrial dynamics and bioenergetics contribute to the aging process by impairing cellular energy production and increasing oxidative stress, leading to cellular and tissue dysfunction.", + "question": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_6 new file mode 100644 index 0000000..8d0e520 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2006 - Beyond the evolutionary theory.pdf", + "2011 - Genomics of human longevity.pdf", + "2023 - Genome-wide RNA polymerase stalling.pdf", + "2009 - High tandem repeat content in the genome of the short-lived.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2007 - Impaired Genome Maintenance Suppresses.pdf", + "2006 - Genomic Instability.pdf", + "2003 - Lifelong voluntary exercise in the mouse prevents.pdf" + ], + "extraction_id": [ + "a933e419-b369-5de5-8236-a1944a486e51", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "373c0bb8-f6b2-5c6b-b768-226b12ba6385", + "89586b79-902d-5e2b-9b8a-b7a8c4971783", + "31088092-778f-59e0-a9de-5ec25c241aab", + "fcb05f39-0821-56e1-a627-92911d4d46bc", + "8f165f13-b4a5-5553-a992-f4a70b079898", + "74482eef-9eb3-5915-838e-5f1f0439c410", + "634526cb-daa7-5769-a3f2-741931964ccd", + "b6422281-0ef4-58f3-9d43-4c8c7534e057" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "a8da3f57-a8dc-55c3-9dc9-eb778105e680", + "2e038219-fdaa-506f-9cd3-51379054130e", + "78812a12-8d31-5159-8367-b0d38e5bc84b", + "bcc64bfb-9b7f-5f6f-83f3-861ab8f8a8e3", + "62b635c3-040e-512a-b016-6ef295308a1e", + "4ed9d527-4f92-51a3-a5d7-6caab655b1be", + "c9c9a8d6-2daf-5ff2-86bd-84e087ba1a47", + "24d4f270-f45b-5830-84f9-b1e5bcd3c070" + ], + "id": [ + "chatcmpl-AIHWn49FE1NOTaexKIcZmCPOm6e2F", + "bc91a693-0eff-5911-ae9a-b192f1088119", + "8ac8b243-f23c-596d-add2-441df4e980a9", + "759ea147-5ac2-5d48-80f2-3693f56d4afc", + "fc227aaf-85c1-553f-aa59-d9bcdd803aaf", + "a0198ed1-1303-5652-aafc-1a1287914ac4", + "e3a78ec1-7f79-55db-a13d-196f718f8a1d", + "bdebc11c-26ca-5ac0-bab3-503bd7d25f50", + "9868d78e-6151-5383-9d52-542a8b43c50f", + "58d61a19-d5b0-501c-90a9-2eeb66866c07", + "e51c4436-0895-5adb-8a80-a3e1ee6956dd" + ], + "contexts": [ + "the attention of researchers as a therapeutic target for age-related diseases [109]. Resveratrol, a phytochemical enriched in the skin of red grapes and wine, has been actively investigated to determine whether it promotesSIRTs activity with conse- quent beneficial effects on aging [110]. IGF Because insulin/IGF-1 function through signaling as a nutrient sensor and controls the transcription of stress response genes, the insulin/IGF-1 pathway provides a", + "the use of lowered IGF signaling (e.g., by target-ing IGF receptors) to treat certain age-related diseasessuch as cancer (Pollak et al., 2004), Alzheimers disease(Cohen et al., 2009), and autoimmune diseases (Smith,2010). Moreover, a number of genes and pathways associ-ated with longevity and CR are part of nutrient-sensingpathways that also regulate growth and development, in-cluding the insulin/IGF1/GH pathway (Narasimhan et", + "as insulinIGF-1 signalling [6], cellular senescence [4], protein refolding [4345] , autophagy [41] and phase 1 and 2 detoxication [36,37,52] . These represent major points of intervention against ageing-related disease. As shown here, lifespan pathways control improved cellular maintenance, which leads to slowed ageing(e.g. slowed normal cognitive ageing) and protection against diseases of ageing (e.g. neurodegenerative diseases of ageing, such as Alzheimers and Parkinsons", + "ent-sensing pathways such as insulin/insulin-likegrowth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan exten- sion, and also the extension of lifespan by DR [ 2]. An interesting observation from the perspective ofhuman ageing is that, in rodents and monkeys, dietsrestricted in glucose, fat or protein uptake reduced ordelayed the risk of cancer and metabolic disease,thus extending the healthspan of the animals [ 2]. Fol-", + "43. Svensson, J. et al. Liver-derived IGF-I regulates mean life span in mice. PLoS ONE 6, e22640 (2011). 44. Junnila, R. K., List, E. O., Berryman, D. E., Murrey, J. W. & Kopchick, J. J. The GH/IGF-1 axis in ageing and longevity. Nat. Rev. Endocrinol. 9, 366376 (2013). 45. Yuan, R. et al. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 8, 277287 (2009). 46. Zhu, H. et al. Reference ranges for serum insulin-like growth", + "5. Piper MD, Selman C, McElwee JJ, Partridge L: Separating cause from effect: how does insulin/I GF signalling control lifespan in worms, flies and mice? J Intern Med 2008, 263:179-191. 6. Holzenberger M, Kappeler L, De Magalhaes Filho C: IGF-1 signaling and aging. Exp Gerontol 2004, 39:1761-1764. 7. Zahn JM, Kim SK: Systems biology of aging in four species. Curr Opin Biotechnol 2007, 18:355-359. 8. McElwee JJ, Schuster E, Blanc E, Piper MD, Thomas JH, Patel DS,", + "humans enriched for familial longevity. Aging Cell. 2016;15(6):112631. 44. Lee WS, Kim J.Insulin-like growth factor-1 signaling in cardiac aging. Biochim Biophys Acta Mol basis Dis. 2018;1864(5 Pt B):19318. 45. Balasubramanian P, Longo VD. Growth factors, aging and age-related diseases. Growth Hormon IGF Res. 2016;28:668. 46. Suzuki K, etal. Serum insulin-like growth factor-1 levels in neurodegenerative diseases. Acta Neurol Scand. 2019;139(6):5637.", + "paradigms for lifespan extension (C. elegans, D. melanogaster), genetic interference in the insulin-signaling pathway can prolong life multi-fold [47,48]. In mammals, IGF1-decient, Ames and Snell dwarf mice (characterized by defects in the development of the anterior pituitary due to mutations in the Prop-1 and Pit1 loci and diminished levels of GH, thyroid stimulating hormone, and prolactin hormone) combine", + "the role of IGF-1 in life span regulation is complex. In theory,SIRT6 might play a role in insulin signaling, similar to Sir2 fac- tors in other lower organisms. However, as in the prematureaging mouse models described above, it remains unclear whether the altered serum IGF-1/insulin levels of SIRT-6- decient mice directly contribute to aging-like phenotypesor, alternatively, reect compensatory alterations. In this re- gard, it will be of interest to determine whether SIRT6 is", + "lin-like growth factors (IGFs), and receptors in theinsulin-signaling pathway has been shown to confergreater longevity in yeast (12, 16), nematodes (21, 44),fruit ies (10, 43), mutant long-lived mice (4, 11), and caloric-restricted mice (40). Therefore, the as-yet un-identi ed mechanism of insulin signaling on lifespan" + ], + "answer": "The insulin/IGF-1 signaling pathway has been identified as a significant target for extending healthspan and lifespan due to its role as a nutrient sensor and its control over the transcription of stress response genes [1]. Here are the therapeutic potentials and challenges associated with targeting this pathway: ### Therapeutic Potentials: 1. **Treatment of Age-Related Diseases**: Lowering IGF signaling, such as by targeting IGF receptors, has been proposed as a treatment for age-related diseases including cancer, Alzheimer's disease, and autoimmune diseases [2]. This suggests that modulating this pathway could have broad therapeutic applications in managing diseases associated with aging. 2. **Lifespan Extension**: Genetic interference in the insulin-signaling pathway has been shown to prolong life in various organisms, including C. elegans, D. melanogaster, and certain mouse models [8]. This indicates a potential for extending lifespan through targeted interventions in this pathway. 3. **Improved Cellular Maintenance**: The insulin/IGF-1 signaling pathway is involved in processes such as cellular senescence, protein refolding, and autophagy, which are crucial for cellular maintenance and protection against aging-related diseases [3]. Enhancing these processes could lead to slowed aging and improved healthspan. ### Challenges: 1. **Complexity of the Pathway**: The role of IGF-1 in lifespan regulation is complex, and it is not fully understood how alterations in this pathway contribute to aging phenotypes [9]. This complexity poses a challenge in developing targeted therapies without unintended consequences. 2. **Balancing Growth and Longevity**: The insulin/IGF-1 pathway is also involved in regulating growth and development. Therefore, interventions that reduce IGF signaling must carefully balance the trade-offs between promoting longevity and maintaining necessary growth functions [2]. 3. **Species-Specific Responses**: While interventions in the insulin/IGF-1 pathway have shown promising results in model organisms, translating these findings to humans is challenging due to species-specific differences in the pathway's role and regulation [8]. Overall, while targeting the insulin/IGF-1 signaling pathway holds significant promise for extending healthspan and lifespan, it requires careful consideration of the pathway's complexity and the potential trade-offs involved.", + "question": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_7 new file mode 100644 index 0000000..4f86bf2 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Immunity and lifespan answering.pdf", + "2018 - Metabolomic pathways to osteoporosis in middle-aged women A genome-metabolome-wide.pdf", + "2017 - An integrative metabolomics.pdf", + "2017 - An integrative metabolomics.pdf", + "2022 - A review on the application of the exposome.pdf", + "2017 - An integrative metabolomics.pdf", + "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "2017 - An integrative metabolomics.pdf", + "2019 - Undulating changes in human plasma proteome.pdf", + "2018 - Spontaneous DNA damage to the nuclear genome promotes senescence.pdf" + ], + "extraction_id": [ + "d4db0b82-40d3-5341-ad30-c70a91fdc785", + "e92950f9-a8d6-5aa5-bf83-ab1cef74627d", + "09a73df7-f690-5984-a498-69a8077fe327", + "af201c05-daed-5cba-abc8-e714483e602f", + "cac0d599-4e0a-5826-b47f-e71b52203956", + "f9c942d2-a191-52d4-8018-1030e414649d", + "6794bfa0-86ff-506f-ac40-35a9b1e33bcf", + "500f52f7-9205-5859-a156-6d30575a3d62", + "24e63f26-0bac-59d4-b325-9c8ead69a4de", + "40e2d528-9297-575f-82a9-178aae0bab81" + ], + "document_id": [ + "a834e7ee-7bab-5c4d-a236-b570d1ae635f", + "f9aa8a09-5148-5399-b6be-c3350f12c0f3", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "803a14cc-d8ab-54ca-80d6-78f1677457f9", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "cb0831f4-540a-5620-b69e-03d6127f84e5", + "53c3130f-7029-50de-8dba-8714dfa36420", + "08be7274-78a3-5e93-9e8c-3d4f6dbeacf9" + ], + "id": [ + "chatcmpl-AIHX1EytrrBFzyZb7piMsWydaKzhq", + "a8194abc-51ab-5c29-a6be-f34bb24e0b47", + "1d8fd475-f7a7-55c6-881e-6985826c1e23", + "4547b6ad-efaf-509e-8e0b-5587542905fd", + "3dba594a-b79b-5bc6-95f6-6e0a36193818", + "ce9d4d88-2586-5071-bf9e-45b7172b0e8e", + "beea72ed-e213-5877-8144-d0ef000a2912", + "6ad38ef0-c6bd-5b6a-9fb6-53c04f18a76d", + "554f2525-a8cb-5003-be3d-137da97ea97f", + "d0b9df07-f6aa-52a5-9696-81f9034d9548", + "07a5111b-b38b-5e1a-bd76-9372499a4dd9" + ], + "contexts": [ + "learning to show that plasma proteins that predict age are predominantly associated with immunity [91]. State-of-the-art metabolomics approaches are also now allowing age-related changes in me- tabolite pro les to be studied, which provide new insights into the physiological mechanisms of age- ing [ 92,93]. The integration of multiple datasets generated from genomes, epigenomes, transcriptomes, proteomes, and metabolomes, an approach termed multi-omics , offers great", + "13. Menni C, Kastenmuller G, Petersen AK, et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol 2013;42:1111- 9. 14. Evans AM BB, Liu Q, Mitchell MW, Robinson RJ, et al. . High Resolution Mass Spectrometry Improves Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High- Throughput Profiling Metabolomics. Metabolomics 2014;4:132.", + "Due to the mild adaptions, the identification of func- tionally altered metabolic activity in aged skin interpret- ation of significant metabolite and transcript changes of small magnitude is especially challenging. Therefore, we employed the previously presented locality scoring ap- proach [60] to identify age-dependent transcriptional al- terations of enzymes that functionally effect proximal metabolic activity and thus metabolite levels. This inte- grated analysis revealed age-dependent, concerted me-", + "matched transcriptome and metabolome data highlighted transcriptionally-driven alterations of metabolism during aging such as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis. Together, we identified several age-dependent metabolic alterations that might affect cellular signaling, epidermal barrier function, and skin structure and morphology.", + "used to assess biological responses provides new oppor - tunities to understand the impact of the environment on the risk of age-related diseases. For example, the multi - omics analysis and integration method produces a pri - ority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome. Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagno - ses and prognoses [78].", + "summary, we identified age-dependent changes in gene expression in different metabolic pathways that have been associated with epidermal homeostasis and there- fore might be important to sustain epidermal function. Integrated analysis of transcriptome and metabolome data Since the age-dependent adaptations of metabolite and transcript levels are only mild, we set out to identify metabolic enzymes that featured an age-dependent and functional change in activity driven by altered gene ex-", + "These high throughput prof iling experiments have gener- ated large amounts of data for meta-analysis [24], which can compare molecular functions and expression patterns that change during aging in different systems. However, such studies are far from exhaustive, as they only describe the molecular changes during aging, which could in fact be the consequence of aging, rather than the cause of aging. Thus to explore the causal factors for aging, studies are increasingly", + "over, the integration of trans criptome and metabolome data revealed a transcriptionally re gulated reduction in protein as well as polyamine biosynthesis and adaptation in upper glycolysis and glycerolipid biosynthesis in aged skin. Results Differences in the epidermal skin metabolome of young and old human volunteers To chart metabolic adaptations in human skin during aging in vivo , we performed non-targeted metabolomicsanalysis of epidermal skin tissue samples obtained from", + "proteomes overlap significantly with the waves of aging proteins (Supplementary Table 15). Accounting for heterogeneous and com - plex changes to the plasma proteome during life will likely improve the sensitivity and specificity of prognostic and diagnostic tests. Moreover, these results are pertinent when considering the use of blood or blood products to treat aging and age-related diseases 39. Specifically, identifying plasma proteins that promote or antagonize", + "rmed using authentic standards. One of the key nodes identi ed by metabolomics as signi cantly altered with accelerated and normal aging was glutathione metabolism ( Fig. 4A), a key antioxidant and index of oxidative stress [71]. Dierential MS was used for proteomics analysis to identify redox- related proteins signi cantly altered in the livers of 3 4 month-old progeroid Ercc1/mice and old WT mice (> 2 years-old) vs. adult WT mice. Expression of catalase, SOD1 (CuZnSOD) and SOD2 (MnSOD)" + ], + "answer": "The integration of proteomics and metabolomics data can provide a comprehensive understanding of age-associated metabolic shifts by revealing changes in protein expression and metabolite profiles that occur with aging. This multi-omics approach allows for the identification of specific pathways and molecular mechanisms that are altered as organisms age. 1. **Proteomics Insights**: Proteomics data can identify plasma proteins that predict age and are predominantly associated with immunity [1]. This suggests that changes in protein expression related to immune function are significant in the aging process. 2. **Metabolomics Insights**: Metabolomics approaches enable the study of age-related changes in metabolite profiles, providing new insights into the physiological mechanisms of aging [1]. For example, metabolomics has identified significant alterations in glutathione metabolism, a key antioxidant pathway, which is indicative of oxidative stress associated with aging [10]. 3. **Integrated Analysis**: By integrating transcriptome and metabolome data, researchers have identified transcriptionally-driven alterations in metabolism during aging, such as changes in glycolysis and glycerolipid biosynthesis, and reductions in protein and polyamine biosynthesis [4], [8]. These changes can affect cellular signaling, epidermal barrier function, and skin structure and morphology, highlighting the interconnected nature of metabolic pathways and their impact on aging. 4. **Functional Changes**: The integration of these datasets can also reveal age-dependent changes in the activity of metabolic enzymes, which are driven by altered gene expression [6]. This helps in understanding how mild adaptations in metabolite and transcript levels contribute to maintaining functions like epidermal homeostasis during aging. Overall, the integration of proteomics and metabolomics data provides a holistic view of the molecular changes that occur with aging, allowing for the identification of biomarkers and pathways that could be targeted to mitigate age-related decline.", + "question": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_8 new file mode 100644 index 0000000..43d59ea --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Epigenetics and aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2022 - Predict the role of lncRNA in kidney aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2016 - Epigenetics and aging.pdf", + "2015 - Comprehensive transcriptional landscape.pdf", + "2018 - Mechanisms of Vascular Aging.pdf", + "2022 - Predict the role of lncRNA in kidney aging.pdf" + ], + "extraction_id": [ + "e2fc0d00-8552-5b39-830f-6df7ec7c32e4", + "91434549-bda3-5154-b089-28efed9c1089", + "b45152cc-6626-5989-a1b0-148c137ea1f3", + "3932ada5-6fc4-5354-b52a-60fddbd47d3e", + "67e3cb94-cc30-58fe-8ff7-a9790c74325f", + "cb24a361-e7b4-5d35-a507-12ef65603d1f", + "5221f1f8-1c53-590f-86c2-23ab47ac0cbc", + "45ff5315-7500-5641-9c1a-e03b8aafc2d5", + "e308005e-d6ef-5492-a9a9-95256bb9ccee", + "67e3cb94-cc30-58fe-8ff7-a9790c74325f" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "0d3b0558-289c-5af0-843a-f288d5da3d8c", + "62b635c3-040e-512a-b016-6ef295308a1e", + "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "6f223b7b-d0ed-55d3-be91-a9e704149a94", + "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "0d3b0558-289c-5af0-843a-f288d5da3d8c" + ], + "id": [ + "chatcmpl-AIHX9ExmiM3mDYaf83XTHzQDSE0IN", + "41e5a2ca-1c83-5394-8fbf-c9dcc75e6a51", + "cb309a6a-4566-5de2-9687-cffa2f7737d2", + "8fa044d2-c807-5207-8361-ea22659d8b63", + "e4d9a99d-4d28-5432-8e91-09388ea4b613", + "85a38fea-bd20-5170-bba0-963b12633c55", + "36a2ed56-a0b9-589d-b178-f1515337f1ae", + "577459d5-e2fc-599f-9806-3d18ab6837e6", + "ab28b2fc-4144-5b86-92af-d6054794a0b1", + "90c36562-0443-5100-b710-d750bd365b46", + "c2978dcd-0bab-5ca9-8130-0cdca1cc9330" + ], + "contexts": [ + "lncRNA which overexpression participates in the regulation of age-associated car - diovascular diseases as it is a non-canonical precursor for hsa-miR-4485 and hsa- miR- 1973 microRNAs [62]. These studies demonstrate that not only coding genes (which represent only 2% of the genome sequence) are implicated in aging regula- tion, but also lncRNAs and microRNAs participate in tissue age-related changes. circRNAs are non-coding covalently closed single-stranded transcripts produced", + "(2008). 192. K. Abdelmohsen, A. Panda, M.-J. Kang, J. Xu, R. Selimyan, J.-H. Yoon, J. L. Martindale, S. De, W. H. Wood III, K. G. Becker, M. Gorospe, Senescence-associated lncRNAs: Senescence- associated long noncoding RNAs. Aging Cell 12, 890 900 (2013). 193. S. Kour, P. C. Rath, Long noncoding RNAs in aging and age-related diseases. Ageing Res. Rev. 26,1 21 (2015). 194. R. Johnson, Long non-coding RNAs in Huntington s disease neurodegeneration. Neurobiol. Dis. 46,2 4 5 254 (2012).", + "155 Premature ageing has been associated with altered expression of lncRNAs that participate in the regulation of the telomere length by modulating the TERT activity and synthesis of telomeric repeats [155, 161]. Furthermore, it has been reported that changes in the expression levels of some lncRNAs are associated with the develop- ment of AD [162]. Circular RNAs andAgeing Circular RNAs (circRNAs) are highly conserved covalently closed non-coding", + "interacting with proteins and nucleic acids in order to regulate gene expression (by indirect epigenetic mechanisms or by direct mechanisms acting as antisense tran- scripts or transcriptional coactivators), nuclear location of transcription factors and stabilization of ribonucleoprotein complexes [155]. It has been reported that lncRNAs are important in the regulation of ageing-associated mechanisms in humans and ani-", + "progression. LncRNA H19 was recently reported to play a crucial role in the activation of MAPK and the NF-kB signaling pathway and the induction of atherosclero - sis [3]. lncRNAs play crucial roles in the progression of diabetic nephropathy [12], glomerular disease [13] and renal fibrosis [14]. The lncRNA Arid-IR promotes NF- kB-mediated kidney inflammation by targeting NLRC5 transcription [15]. The cell cycle changes during aging. Previous studies have shown that lncRNAs are related to", + "expression of SIRT1 and are decreased in lymphoblastic cell lines generated from centenarians compared with those of AD patients, suggesting a protective effect of these miRNAs against neurodegeneration [66]. Long noncoding RNAs are important regulators of transcriptional networks and the closed or opened chromatin state [2]. One interesting example of an lncRNA is that associated with aging, H19. This lncRNA interacts with MBD1 (a methyl-", + "associated factors, modulating aging and senescence directly or in-directly. One such example includes a specific lncRNA, Gas5 ,w h i c h is highly expressed in aged mice brain and has been associated with im-paired learning ( 189). Another bona fide example is H19lncRNA, a dif- ferentially spliced product from the H19gene located at the IGF2/H19 imprinted locus, which interacts with methyl-CpG binding domain", + "tempting to speculate that these lncRNAs may exert some regulatory control of this locus, possibly contributing to senescent phenotypes. Together, these findings point to- wards a host of age-related ncRNAs as regulators of aging pathways and networks. Interaction network analysis The increased accuracy and breadth of our RNA-seq data sets allowed us to generate networks of gene func- tional change in aging liver, above and beyond what was observed using DAVID or GOrilla. Using Ingenuity", + "RNAs interact with proinflammatory signaling pathways and regulate senescence; however, their role on regulation of vas-cular aging processes is virtually unknown. 151 Interestingly, there is initial evidence linking the expression of the long noncoding RNA Meg3 (maternally expressed 3) to age-related impairment of angiogenic capacity of endothelial cells.152 Further studies are definitely needed to understand the", + "Page 2 of 11 Lietal. BMC Genomics (2022) 23:254 mechanism of kidney aging will be of great significance for delaying the occurrence and development of renal aging. Although a small number of studies have been conducted on renal aging, it is still meaningful to com - prehend the mechanism of renal aging. Long chain noncoding RNAs (lncRNAs) are more than 200 nucleotides in length. LncRNAs regulate transcrip - tional and posttranscriptional RNA processing, transla -" + ], + "answer": "Long non-coding RNAs (lncRNAs) play significant roles in the regulation of aging and age-related diseases through various mechanisms: 1. **Regulation of Age-Associated Cardiovascular Diseases**: LncRNAs are involved in the regulation of age-associated cardiovascular diseases by acting as non-canonical precursors for specific microRNAs, such as hsa-miR-4485 and hsa-miR-1973, which participate in tissue age-related changes [1]. 2. **Senescence-Associated lncRNAs**: Certain lncRNAs are associated with cellular senescence, a key process in aging. These senescence-associated lncRNAs are implicated in the regulation of aging mechanisms [2]. 3. **Telomere Length Regulation**: LncRNAs are involved in the regulation of telomere length by modulating TERT activity and the synthesis of telomeric repeats, which is crucial for cellular aging and longevity [3]. 4. **Gene Expression Regulation**: LncRNAs interact with proteins and nucleic acids to regulate gene expression through epigenetic mechanisms, acting as antisense transcripts or transcriptional coactivators. They also influence the nuclear location of transcription factors and stabilize ribonucleoprotein complexes, which are important in aging-associated mechanisms [4]. 5. **Disease Progression**: LncRNAs play roles in the progression of various age-related diseases, such as atherosclerosis, diabetic nephropathy, glomerular disease, and renal fibrosis. For example, lncRNA H19 is involved in the activation of signaling pathways that induce atherosclerosis [5]. 6. **Neurodegeneration**: LncRNAs are implicated in neurodegenerative diseases, such as Huntington's disease, by regulating transcriptional networks and chromatin states [6]. 7. **Impaired Learning and Senescence**: Specific lncRNAs, like Gas5, are associated with impaired learning in aged brains, and others, like H19, interact with methyl-CpG binding domains, influencing senescence and aging pathways [7]. 8. **Angiogenic Capacity**: The expression of lncRNA Meg3 is linked to age-related impairment of the angiogenic capacity of endothelial cells, indicating a role in vascular aging processes [9]. Overall, lncRNAs are crucial regulators of aging and age-related diseases through their diverse roles in gene expression, cellular senescence, disease progression, and other aging-related mechanisms.", + "question": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_9 new file mode 100644 index 0000000..b9a9aea --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_aging_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf" + ], + "extraction_id": [ + "fcc88af4-1949-59fe-8111-200ec0dcb7d6", + "c072d600-8450-5842-ade1-aefd03854312", + "8db25d5e-25bd-5873-a53d-3815badbfd32", + "267468ed-0f9f-5a55-9334-9630792f300d", + "625c559f-9ef6-5bef-8b4c-c57a72d421ed", + "7d0ed573-4d0a-5de2-8be2-1ec0fb3a5800", + "1caf6ac0-0409-5b28-8fcf-bdffff2738a8", + "5f85264a-a5cd-5ef6-a4c9-900dcb7b07ad", + "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "267468ed-0f9f-5a55-9334-9630792f300d" + ], + "document_id": [ + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "70945353-4808-539a-80f9-5632c27913e5", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "62b635c3-040e-512a-b016-6ef295308a1e", + "70945353-4808-539a-80f9-5632c27913e5", + "62b635c3-040e-512a-b016-6ef295308a1e" + ], + "id": [ + "chatcmpl-AIHXK8F2Ohi1RX10guI90pglYXyhM", + "9e4d48fb-e942-52a6-8e7e-57313d567a72", + "d7a12958-6d0b-546f-b0aa-152b6812e2fd", + "093e7604-5108-5fda-850e-007817090a9a", + "9a06df0b-a5b6-52d8-82c1-9dda446f9132", + "49c65d89-ec44-5412-a5bf-d94649e4afc3", + "a5ffc379-24d5-5c73-8435-41ca43af6347", + "7387d1f6-323a-52ea-90d4-6821fea31bf9", + "a02244c8-44da-595f-8a61-42bae541d784", + "4eb34c07-921b-55bb-98eb-ff013bb2ace0", + "c6c119e6-362e-5ae7-a1f1-a5e75eb456ba" + ], + "contexts": [ + "models of ageing, but it will also drastically accelerate the generation of refined ver - sions of those models or even allow the development of new research approaches in non-model organisms. Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against 11 Applications ofCRISPR-Cas inAgeing Research", + "of ageing. Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating disease- associated gene pathways. S. Haston et al.", + "ularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be beneficial in clari- fying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identification of new potential therapeutic targets. In fact, epigenetic drugs are of particular interest to the clinic due to their reversible and transient effect. A limitation of manifold epigenetic studies, however, are the variations among sin-", + "224 high-throughput assays able to further delineate important molecular pathways involved in inducing and maintaining cellular senescence in both physiological ageing and age-associated diseases. Applications ofCRISPR-Cas intheStudy ofAgeing-Related Disease Cardiovascular Disease One of the most notable contributions of CRISPR-Cas to ageing research is its ability to target non-proliferating cells (contrary to HDR-directed gene targeting),", + "219 Applications ofCRISPR-Cas inBasic Research oftheMolecular Causes ofAgeing Investigating theMechanisms ofLongevity Currently there have been no studies exploring the utility of the CRISPR-Cas sys- tem on experimentally extending the lifespan of physiologically aged laboratory animals. A main issue in this regard is that established vertebrate models already possess relatively long lifespans that make longevity extension studies economi-", + "CRISPR-Cas genome- editing tools will provide feasible implementation of 11 Applications ofCRISPR-Cas inAgeing Research", + "the basis for future investigations into the spatio-temporal dynamics of the telom- erase protein invivo.11 Applications ofCRISPR-Cas inAgeing Research", + "induced by telomere erosion. Protein Cell. 2019;10:3705.11 Applications ofCRISPR-Cas inAgeing Research", + "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in molecular biology and cell culture approaches (for instance Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes across species. An improved understanding of epigenetic mechanisms affecting longevity will be deciding crucial step towards the identication of new potential therapeutic targets. In", + "In recent years, CRISPR-Cas technologies have significantly contributed to studies addressing the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimers disease (AD) and Parkinsons disease (PD). Currently, it has mostly been utilised for developing new or improved tools in which to study the molecular mechanisms underlying these diseases, such as in patient-derived cell lines carrying pathogenic mutations." + ], + "answer": "Recent advancements in CRISPR/Cas9 technology have opened new avenues for studying and potentially reversing aging in several ways: 1. **Development of New Research Models**: CRISPR/Cas9 is significantly impacting research by enabling the creation of new models for studying age-related diseases. This includes manipulating disease-associated gene pathways, which can lead to a better understanding of the cellular and molecular origins of these diseases [1], [2]. 2. **Understanding Aging Processes Across Species**: The technology is beneficial in clarifying aging processes across different species. This improved understanding, particularly of epigenetic mechanisms affecting longevity, is crucial for identifying new potential therapeutic targets [3], [9]. 3. **Targeting Non-Proliferating Cells**: One notable contribution of CRISPR/Cas9 to aging research is its ability to target non-proliferating cells. This capability is important for studying cellular senescence, which is a key factor in both physiological aging and age-associated diseases [4]. 4. **Molecular Pathogenesis of Neurodegenerative Diseases**: CRISPR/Cas technologies have significantly contributed to studies on the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. This includes developing new tools to study the molecular mechanisms underlying these diseases using patient-derived cell lines with pathogenic mutations [10]. These advancements suggest that CRISPR/Cas9 technology not only aids in understanding the mechanisms of aging but also holds potential for developing interventions that could reverse or mitigate age-related conditions.", + "question": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_1 new file mode 100644 index 0000000..e7ad2ee --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2009 - Metabolomics Applied to Diabetes Research.pdf", + "2014 - The potential of novel biomarkers to improve risk prediction of type 2 diabetes.pdf", + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "2013 - Variants of Insulin-Signaling Inhibitor Genes.pdf", + "2021 - A genome-wide association study identifies 5 loci associated with frozen shoulder and implicates diabetes as a causal risk factor.pdf", + "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "2014 - The potential of novel biomarkers to improve risk prediction of type 2 diabetes.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf" + ], + "extraction_id": [ + "8b15673a-deaf-5e34-945c-ea2a1365552d", + "380e9a2e-8f9f-5f9e-ba20-3695b1c60fda", + "75485c9d-6c66-52fe-8fb1-e6d2440a7f49", + "8b15673a-deaf-5e34-945c-ea2a1365552d", + "7cec13b8-d349-5ea4-b866-17fc760d364c", + "f258a3c5-02d6-5f8f-a989-27f6c795145c", + "2052d37d-f778-53e2-a2f9-9e4311e8a953", + "97b6d492-9139-50ec-9685-53a803f5c995", + "496d9615-7530-530c-bea1-62fe63ea54ca", + "751ccb98-2846-5ca7-8ab8-2684100c28fa" + ], + "document_id": [ + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "a6ae2fb6-88ae-588f-a98d-b6092f886ed9", + "2bc2f4be-378f-5ced-8288-e2a132a94540", + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "d43a59e8-fe3b-503a-863b-235af8790f2a", + "8276e137-4591-51bd-9351-f4d27d3b35da", + "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "2bc2f4be-378f-5ced-8288-e2a132a94540", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d" + ], + "id": [ + "chatcmpl-AIHIPLyXp5Go74Qys43ojpQ0czAzb", + "012b6e5f-ab45-53aa-a392-45a46916e752", + "aaf89eb0-09a8-517d-b8ae-4e76a8211be6", + "6919bc75-2637-5359-9c05-96d192be8c4e", + "93455356-fe0b-58f4-9ae7-58f932d33560", + "cfc35db4-346c-55fd-b0bc-fa3cac307731", + "3b5c1a49-cb11-57ef-9046-e3c8f7af589e", + "b74d0bb9-eb0d-59bb-8a37-d3425d5591a2", + "ead10261-182f-5ab1-9af0-ce8a17677d4a", + "4971b4de-b190-56b5-b7b6-64b2c8e2a565", + "01a2230a-b91d-57b6-b138-7aae805f4383" + ], + "contexts": [ + "proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way. The ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to di erent glucose-lowering therapies. Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes.", + "Metabolomics Applied to Diabetes Research Moving From Information to Knowledge James R. Bain, Robert D. Stevens, Brett R. Wenner, Olga Ilkayeva, Deborah M. Muoio, and Christopher B. Newgard Type 2 diabetes is caused by a complex set of interactions between genetic and environmentalfactors. Recent work has shown that human type2 diabetes is a constellation of disorders associ- ated with polymorphisms in a wide array of genes, witheach individual gene accounting for /H110211% of disease risk", + "between protein signals and type 2 diabetes incidence. Acta Diabetol. doi: 10.1007/s00592-012-0376-3 82. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB (2009) Metabolomics applied to diabetes re-search: moving from information to knowledge. Diabetes 58: 2429 244383. Suhre K, Meisinger C, Dring A et al (2011) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953", + "The future: genetics, epigenetics, and omics Although understanding of the genetics of type 2 diabetes has advanced rapidly, much remains unknown. How genes interact with the environment to cause progressive loss of -cell function is unclear. Environmental factors and hyperglycaemia could contribute to epigenetic changes in DNA and histones, thereby modifying gene expression in organs implicated in the pathogenesis and progression of type 2 diabetes, including in cells. 82,83", + "potential to make far-reaching contributions to our understanding of molecular basis of T2D and the development of novel strategies for patient care. 2.1 Introduction Type 2 diabetes (T2D) is a common, chronic disorder whose prevalence is increas-ing rapidly across the globe. Like other complex diseases, T2D represents achallenge for genetic studies aiming to uncover the underlying pathophysiological mechanisms. It is predicted that T2D will affect 592 million individuals by 2035", + "inthepathogenesisoftype2diabetesandmetabolism, Current Opinion in Clinical Nutrition and Metabolic Care ,vol.10,no .4, pp .420426,2007 . [110] M.C.Cornelis,E.J.T.Tchetgen,L.Liangetal.,Gene-environ- ment interactions in genome-wide association studies: a com- parative study of tests applied to empirical studies of type 2 diabetes, American Journal of Epidemiology ,v o l.17 5,no .3,p p . 191202,2012. [111] M.L.Metzker,Sequencingtechnologiesthenextgeneration, Nature Reviews Genetics ,vol.11,no.1,pp.3146,2010.", + "meta-ana lysis provides insight intothegenetic architecture oftype2diabetes susceptibility. NatGenet. 2014; 46:234 244. https://doi.or g/10.103 8/ng.2897 PMID: 24509480 26. Morris AP,Voight BF,Teslovich TM,Ferreira T,Segr A-V, Steinthorsdot tirV,etal.Large-sc aleassoci- ation analysis provide sinsights intothegenetic architecture andpathophysi ology oftype2diabetes. NatGenet. 2012; 44:981 990. https://doi.or g/10.103 8/ng.2383 PMID: 228859 22", + "monitoring and preventing progression to costly co-morbidities. The principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established. It is not our goal here to show this once again. The questions we ask are rather How well are different approaches suited to attain this goal? and What are optimal settings under which such studies can be successful?. Others have already investigated these questions before [16,17,18]. However, we", + "Owing to current advances in -omics technologies, such as genomics, transcriptomics, proteomics and metabolomics, the number of candidate biomarkers keeps growing; however, only a small proportion of these has been investigated withreference to their potential to improve the prediction of type 2 diabetes. Genetic variants The heritability of glycaemic traits and type 2 diabetes is high [40], and the large genome-wide association studies published to date since the first in 2007, based on up to >10 5study", + "have improved our understanding of the complexity of T2DM pathophysiology, beyond the classic triumvirate of -cell, skeletal muscle and liver87. However, the ability of these biomarkers to predict future risk of T2DM beyond anthropometric measures, lifestyle factors and fasting levels of glucose and lipids is still debatable87. Within the past 7years, a complementary, novel set of T2DM biomarkers has largely been generated by metabo- lomic studies, which systematically analyse metabolites" + ], + "answer": "Recent advancements in multi-omics approaches, including proteomics and metabolomics, have significantly enhanced our understanding of the pathogenesis of Type 2 diabetes (T2D). These approaches allow for a comprehensive analysis of the molecular components involved in T2D, providing insights into the disease's complexity and heterogeneity. 1. **Integration of Omics Data**: The integration of data from proteomics, genomics, and transcriptomics helps in understanding the pathophysiology of T2D and the varied responses to different glucose-lowering therapies. This holistic view is crucial for identifying the molecular underpinnings of T2D and tailoring personalized treatment strategies [1]. 2. **Metabolomics Insights**: Metabolomics studies have identified specific metabolites, such as branched-chain and aromatic amino acids, that are associated with obesity and T2D. These findings suggest that metabolic alterations play a significant role in the development of T2D, offering potential targets for therapeutic intervention [1]. 3. **Complex Interactions**: T2D is caused by complex interactions between genetic and environmental factors. Metabolomics has helped elucidate these interactions by identifying metabolic footprints associated with T2D, thus moving from mere information gathering to actionable knowledge [2], [3]. 4. **Biomarker Discovery**: Advances in omics technologies have led to the discovery of numerous candidate biomarkers for T2D. Although only a small proportion of these biomarkers have been thoroughly investigated, they hold promise for improving the prediction and management of T2D [9]. 5. **Beyond Traditional Models**: The use of metabolomics has expanded our understanding of T2D beyond traditional models focused on the pancreas, liver, and muscle. This broader perspective is essential for developing more effective prevention and treatment strategies [10]. Overall, these advancements in multi-omics approaches provide a more detailed and nuanced understanding of T2D pathogenesis, paving the way for improved diagnostic and therapeutic options.", + "question": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_10 new file mode 100644 index 0000000..2c2d248 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2008 - Glossary of Genetics Genomics Terms.pdf", + "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf", + "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2010 - Evidence of Interaction between Type 2 Diabetes.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf" + ], + "extraction_id": [ + "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "48c3e4a4-db23-5fca-9c46-775e80894655", + "52a000e5-d790-55f2-9eac-14554d426173", + "b24927c4-ee83-51a8-b431-b43be7d3b678", + "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "455b92f7-6156-5735-8586-29a66af0f9e5", + "d2de4ed1-897b-5e5b-bc29-c03310096d64", + "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "cb5c4aab-77ed-58cd-98b8-9e1ba64eb9cf", + "89bf4316-d0cc-5310-a45e-1dd8b8aefe1b" + ], + "document_id": [ + "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "6d341cd2-ae56-5807-9aff-39298efc4d06", + "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "1a33b1d1-23ee-5b33-b42d-c745c8210166", + "8c310d76-0a3b-574c-9859-859258870ee5", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ea9601ed-ad83-506e-b1b7-e7211671ff73" + ], + "id": [ + "chatcmpl-AIHJknEcr96E1ybbJw2DE0EMMQI1v", + "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "55f842a4-506a-5992-9b6e-47c81aee6809", + "728c47bb-e8e2-5359-9ff5-9ad9b13f999c", + "15872da6-8175-5db6-b741-10ae3cf85088", + "53fd1ea0-5ca7-5066-bb07-e7469c640e22", + "027f0c97-d38d-551d-add3-4a759a406895", + "155260c5-ba90-540f-8d48-bafece83fa47", + "3d00ac57-9828-5146-a895-9840de9af5f7", + "518d294f-67c5-5870-9f28-3cb4dfa81e42", + "6b83f0af-1145-5679-9dae-0f645771d25d" + ], + "contexts": [ + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "ponse to thiazolidinedione therapy and candidate genes [100103]. Results from pharmacogenetic studies could potentially provide physicians with a powerful tool to adjust therapy appropriately for those individuals carry ing variants known to affect a given medication. Distefano and Watanabe have recently reviewed the pharmaco genetics of diabetes [104]. Genegene and geneenvironment interactions are also likely to be helpful to the clinician in making therapeutic", + "Genomics of T2D Diet, lifestyle, environment, and even genetic variation influence an individuals response to disease therapy. Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for iden - tifying genetic variants responsible for patient differ -", + "ease caused by interactions between multiple genetic and environmental factors. Significant progress has been made in understanding the genetic architecture of T2D over the past 10 years [1]. A number of genome-wide as- sociation studies in diverse human populations have identified more than 60 common variants and loci asso- ciated with risk for T2D [2]. These studies have also revealed a significant overlap between traits and pheno- types of monogenic diabetes with related common", + "21582171 (2014). 29. Wood, A. R. et al. A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants. Diabetes 66, 22962309 (2017). 30. Pickrell, J. K. Joint analysis of functional genomic data and genome- wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559573 (2014). 31. Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets", + "by GWASs [ 16,28,29]. A wide variety of network-based approaches have been applied to investigate the extent to which the genetics of T2D predisposition converge on a restricted set of biological pathways. Several T2D risk variants have been identied as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. [ 10,16]. The newly discovered SNVs allow the better characterization of abnormalities in early insulin processing and secretion. TCF7L2 ,SLC30A8 ,C2CD4B ,", + "[10] , many environmental factors [11] , and the interac- tions among those genetic and environmental factors. Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant pro- portion of the susceptibility genes identified by GWASs will interact with these environmental factors to influ-ence the disease risk. Florez et al.", + "interactions suggest a way by which genetic risk may beameliorated, these environmental factors are of great relevanceto public health, and are the focus of a growing number of studies [7]. Environmental factors, such as diet and lifestyle, are important in the onset, development and progression of T2D and its related phenotypes [8,9]. The interactions of environmental factors with", + "cases. J Am Med Assoc. 1956;161:1628 30. 3. Duncan LE, Keller MC. A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am J Psychiatry. 2011;168:1041 9. 4. Brito EC et al. Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glu- cose regulation and type 2 diabetes: a study of 16,003 Swedish adults. Diabetes. 2009;58:1411 8.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate" + ], + "answer": "Recent studies on the interaction between genetic variants and environmental factors in diabetes development have identified several potential therapeutic targets. These include: 1. **Primary Regulators of Insulin Secretion and Action**: Several type 2 diabetes (T2D) risk variants have been identified as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. This suggests that targeting these pathways could be a potential therapeutic strategy [6]. 2. **Specific Genetic Variants**: Newly discovered single nucleotide variants (SNVs) allow for better characterization of abnormalities in early insulin processing and secretion. Genes such as TCF7L2, SLC30A8, and C2CD4B have been highlighted as potential targets due to their roles in these processes [6]. 3. **Gene-Environment Interactions**: The interaction between genetic susceptibility and environmental factors such as physical activity and dietary fat has been shown to modify the risk of glucose homeostasis and T2D. This indicates that interventions targeting these environmental factors could potentially mitigate the genetic risk [7]. These findings underscore the importance of considering both genetic and environmental factors in developing therapeutic strategies for diabetes.", + "question": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_11 new file mode 100644 index 0000000..9a6b3cb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2016 - The genetic architecture of type 2 diabetes.pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf", + "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf" + ], + "extraction_id": [ + "01778b74-61b2-5f64-be8e-775c79af171d", + "0f2a8ab2-1666-50c0-b0b0-2a37e1f6917f", + "01778b74-61b2-5f64-be8e-775c79af171d", + "c266fa33-e779-514b-9337-636a69c6e6a4", + "89a75bbb-f0f6-5391-98fd-56631343a38e", + "524594ab-31ca-5f5c-8126-7c58060bb73e", + "01778b74-61b2-5f64-be8e-775c79af171d", + "5cd40b2d-72d6-5386-be94-b4e8188e4114", + "36db7673-abd7-55b1-9caf-b66498e19e78", + "89a75bbb-f0f6-5391-98fd-56631343a38e" + ], + "document_id": [ + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "d7e2a9de-46f1-5191-9cb0-dd68eb9f365a", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "2a386c81-8f24-5993-8e48-0e89d7fb4fec", + "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c" + ], + "id": [ + "chatcmpl-AIHJu3dzRIlHnqdmlSb6lzwzfrntr", + "935ff4d1-9840-5fab-8ad8-82e668319e91", + "b6905dfe-c622-58cd-b9ac-2cdcedada7e4", + "0ab59821-8bf4-50d3-92e7-b0dd593883a8", + "e266cecd-e881-5c64-8ce3-4894cbe47db5", + "aacfbc09-f4ec-5b71-a4a8-efd43cf8b6db", + "0977e7bd-4c4c-5c6c-a4d1-3b5f6fda03c5", + "9a3d06ce-e86f-511f-82ac-97e486618e47", + "451c2da6-3fd5-53f4-a58e-32b4f1d2cbbd", + "40f471a6-3615-52f3-a306-9f3568680409", + "a5469aca-198e-56f5-ab92-16fd00c5e0fc" + ], + "contexts": [ + "and rare coding variants do not account for much of theheritability of type 2 diabetes. Under this scenario, themissing heritability could be located in common orlow-frequency and rare variants in noncoding regionsof the genome. Recent studies that jointly modeled dia-betes or obesity risk as a function of genetic relatednessacross all of the GWAS SNPs have suggested that much of the heritability of these traits can be explained by", + "T2D heritability. 3. Uncovering the Signicance of Rare-Coding and Non-Coding Genetic Variants in the Etiology of Type 2 Diabetes As previously stated, GWASs have uncovered many new genetic associations that are relevant to T2D, but GWAS ndings represent common and mid-frequency genetic variations, thus excluding rare frequency variants and also cumulative effect of many variants with small effect sizes. Missing heritability refers to the portion of genetic variance that cannot be explained by all signicant", + "could be accounted for by low-frequency and rare variants of moderate effect in a small number of genes. Our whole-exome sequencing study has explicitly addressed thisquestion. Additionally, we did not examine whether thereare fewer than 20 genes involved in type 2 diabetes butrather looked at whether rare coding variants in fewerthan 20 genes account for much of the heritability. In such a model, any number of other genes that do not", + "contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome", + "One common disease that has been subjected to intense genetic study is type 2 diabetes. 32The heritability of type 2 diabetes has been estimated to be around 30%.3335 Through GWASs, 63 loci have been reproducibly associ-ated with type 2 diabetes. 36However, as for other complex traits, the associated SNPs can only account for <20% of the heritability estimated from family studies.36 Here, we seek to evaluate the role that rare coding vari-", + "prevalence of T2D. These authors found rare variants that were not detected previously in population studies, but none of them were associated with T2D [ 49]. Larger multi-population studies and more advanced study methods are needed to reliably identify rare variants that are exclusively associated with T2D to eventually uncover missing T2D heritability. 3.2. Genetic Variants in Familial Studies of Type 2 Diabetes The development of T2D is driven by the combined effect of environmental factors and a", + "variance in disease risk that can be accounted for bythe 63 previously identied associations with commonvariants. Our empirical and simulation results are compatible with a variety of different genetic architectures for type2 diabetes. First, if rare coding variants are responsiblefor the majority of the heritability of the trait, the variants are most likely scattered across many ( >20) different", + "Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D. However, only close to 10% heritability is explained by these variants. Other genetic variants, particularly those which are rare but with significant effects need to be identified.", + "and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294298 (2014). 168. Lek, M. etal. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285291 (2016).169. Xue, A. etal. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018). 170. Huyghe, J. R. etal. Exome array analysis identifies", + "diabetes. In particular, our study suggests that when clus-tered in a small number of genes, rare coding variants ofmoderate to strong effect are unlikely to account formuch of the missing heritability. Rather, if rare coding var-iants are an important factor in type 2 diabetes risk, theyare most likely scattered across many genes. Our resultshave important implications for the design and interpreta- tion of future medical resequencing studies. Subjects and Methods Study Populations" + ], + "answer": "Rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes by potentially explaining some of the \"missing heritability\" that common variants identified through genome-wide association studies (GWAS) do not account for. While GWAS have identified many common variants associated with Type 2 diabetes, these explain only a fraction of the heritability of the disease [4]. The missing heritability could be located in low-frequency and rare variants, particularly in noncoding regions of the genome [1]. However, studies have shown that rare coding variants, especially when clustered in a small number of genes, are unlikely to account for much of the missing heritability [10]. Instead, if rare coding variants are significant, they are likely scattered across many genes [10]. Therefore, while rare variants may contribute to the heritability of Type 2 diabetes, their exact role and impact remain to be fully elucidated, and larger multi-population studies are needed to reliably identify rare variants exclusively associated with Type 2 diabetes [6].", + "question": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_12 new file mode 100644 index 0000000..43f5c75 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2018 - Pilot genome-wide association study identifying novel risk loci for type 2.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2019 - Development and Genome Sequencing.pdf", + "2010 - Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - MALAT1 An Epigenetic Regulator of Inflammation in Diabetic Retinopathy.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf" + ], + "extraction_id": [ + "097b0feb-4ffa-5ad6-8140-5e404e83c80b", + "b1d2c95c-d639-5c75-8c52-278f1e187675", + "a166bf29-6be5-54ff-b869-9d4ff087d1e5", + "79ce3e1f-1c6a-51dc-b5ad-848173af4e69", + "d971dced-935c-566b-a4a2-11bcf99b9c84", + "f9500ec9-0600-5e2c-b64e-b062fb7a7552", + "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "2d8abaf8-9f48-5b9a-b50e-897fd4751b7b", + "efc73cf6-99c6-5272-9bb0-7bd6a34633f0" + ], + "document_id": [ + "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2", + "766edfd5-4756-51bf-b636-c94b041d030c", + "e2c6283c-d95f-574a-9dab-345a708d388c", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "18820c9e-f7ae-57ae-897d-0d9c3f616b6a", + "23f0ee09-5536-5f63-bf15-bce1894b5fed", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "cd4dd3bc-bcea-5670-a40f-bc95c319f3ed", + "019efefb-65db-55f5-a3a7-4f224473f51f" + ], + "id": [ + "chatcmpl-AIHK1iKM6Po4mTFBDwGSbImYGY94p", + "1eb14f0c-3d81-53bc-91c8-98acf2e014b7", + "92a20945-b038-52a4-8cc8-ffb70e6f7559", + "9c11148d-9f7a-5d84-aa05-2b67e7a8f1f3", + "19d9d3a6-c982-5c57-a16c-226b8aa76ed5", + "b774bf7b-4546-56d2-ae7b-7bc2c9f2fb08", + "94eed8ea-cc78-52d0-a188-442380512b85", + "2d9e043b-a3fa-52dc-9a4e-71ed49f9ec1d", + "66b05301-179b-597c-bb68-e6fd0e0d1d5a", + "4a8a2861-62b9-520c-8833-45fb8bd3ffd7", + "25d3616b-1ba4-59ce-a11b-38d108d5b387" + ], + "contexts": [ + "13 De Rosa et al. Type 2 Diabetes and CVD Frontiers in Endocrinology | www.frontiersin.org January 2018 | Volume 9 | Article 2176. Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentia- tion and development. Nat Rev Genet (2014) 15:721. doi:10.1038/nrg3606 177. Wang KC, Chang HY . Molecular mechanisms of long noncoding RNAs. Mol Cell (2011) 43:90414. doi:10.1016/j.molcel.2011.08.018 178. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet (2011) 12:86174. doi:10.1038/nrg3074", + "Epigenetic Mechanisms in Diabetic Complications 16 other non-coding RNAs can also in teract with transcriptional co -regulators and thereby further 337 influence epigenetics and tran scriptional regulation (82, 104). 338 Recent findings have demonstrated a critical role for miRs in various diseases. They have 339 been found to play key roles in proliferation, di fferentiation, development, and in cancer, where 340", + "Beltrami, C., Angelini, T.G., Emanueli, C., 2015. Noncoding RNAs in diabetes vascular complications. J. Mol. Cell. Cardiol. 89, 42 50.https://doi.org/10.1016/j.yjmcc. 2014.12.014 . Brookheart, R.T., Michel, C.I., Listenberger, L.L., et al., 2009. The non-coding RNA gadd7 is a regulator of lipid-induced oxidative and endoplasmic reticulum stress. J. Biol.Chem. 284, 7446 7454. https://doi.org/10.1074/jbc.M806209200 . Carter, G., Miladinovic, B., Patel, A.A., et al., 2015. Circulating long noncoding RNA", + "Noncoding RNAs that are induced by diabetic conditions can also promote theexpression of pathological genes via various post-transcriptional and post-translational mechanisms These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for metabolic memory Key epigenetic regulators, microRNAs and long noncoding RNAs could serve", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "coding RNAs [18]. A number of indirect lines of evi-dence point to the involvement of epigenetic changes indiabetic nephropathy. Murine models of disease progres-sion displaying temporal variation in gene expressionhave indicated these supra-sequence devices may beinvolved in the pathogenesis [19]. Gene expressionchanges reflect dynamic alterations in gene transcription and also messenger RNA stabi lity, which may be influ-", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "expected to rise due to the increasing incidence of diabetes, which necessitates the need for exploration of new molecular aspects of DR to expand the current scope of therapy. In the last two decades, the rapid advent of high-throughput genomic technology has made it evident that more than 97% of the human genome is comprised of non-protein-coding elements, such as non-coding RNAs (ncRNAs) 6. Although significant research has been conducted in annotating the transcripts that arise from these", + "regulation, control of mRNA decay, and sequestration of transcription factors. Although the underlying causes that define the diabetic phenotype are extremely intricate, most of the studies in the last decades were mostly centered on protein-coding genes. However, current opinion in the recent past has authenticated the contributions of diverse lncRNAs as critical regula - tory players during the manifestation of diabetes. The current review will highlight the importance of lncRNAs in regulating" + ], + "answer": "The latest findings on the role of non-coding RNAs in the pathogenesis of diabetes highlight several key aspects: 1. **Role of lncRNAs in Diabetes**: Long non-coding RNAs (lncRNAs) are implicated in mediating complex pathological mechanisms of diabetes. They are involved in post-transcriptional regulation and are associated with orchestrated networks that influence diabetes pathogenesis [5]. LncRNAs are considered better therapeutic targets due to their specific functions in regulating cellular pathways and their expression patterns that correlate with the progression of diabetes [7]. 2. **Epigenetic Influence**: Non-coding RNAs, including microRNAs and lncRNAs, can influence epigenetic mechanisms. They can promote the expression of pathological genes through post-transcriptional and post-translational mechanisms, contributing to metabolic memory and sustained gene expression in diabetic conditions [4]. 3. **Regulation of Islet Function**: LncRNAs have been shown to regulate pancreatic islet function, which is central to understanding diabetes pathophysiology. For instance, the lncRNA H19 has been implicated in islet development and function [8]. 4. **MicroRNAs in Disease**: MicroRNAs (miRs) play critical roles in various diseases, including diabetes, by influencing proliferation, differentiation, and development [2]. These findings underscore the importance of non-coding RNAs as regulatory players in diabetes and its complications, offering potential avenues for therapeutic intervention.", + "question": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_13 new file mode 100644 index 0000000..86280c6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2021 - Genetics and genomics of arrhythmic.pdf", + "2022 - Stability of polygenic scores across.pdf", + "2022 - Coming of Age Human Genomics.pdf", + "2021- Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians.pdf" + ], + "extraction_id": [ + "02701cd5-d2ce-560c-b5a9-e694fecdb3c2", + "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "17c49e58-c89a-5495-b17f-adcade90a4c6", + "3c30b33b-8928-5cee-9c37-c70642fff75c", + "17c49e58-c89a-5495-b17f-adcade90a4c6", + "ada410d0-6b91-5959-b834-cc3389e29c5f", + "a548bb25-cbff-5466-b932-afe160bfbe32", + "d2add072-cb41-54f8-9583-9616b11e4ae3", + "5f2ac528-4965-5d5e-86d0-8862032bb7b9" + ], + "document_id": [ + "4ece243f-acda-569d-b75d-37539260dcb3", + "4ece243f-acda-569d-b75d-37539260dcb3", + "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "4ece243f-acda-569d-b75d-37539260dcb3", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "4ece243f-acda-569d-b75d-37539260dcb3", + "462ed035-e4fb-5847-a92d-927f05a2b58b", + "30af2d38-7941-5d0a-9da1-a8ad2dc22329", + "45506895-eef1-57f4-8ca1-79fe23a2493f", + "ce8040c7-157f-54c5-b28b-3224e8871415" + ], + "id": [ + "chatcmpl-AIHKAjqtg6gr5hkyEsdT3wwz3yXTB", + "748c1d81-0c27-515a-8bf1-12e717645e66", + "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "3b9e0030-8bf9-5d63-9813-3cf18e98be3b", + "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "a374d88e-458e-5252-8b3a-5ca162fa6982", + "a551335d-c3ed-5d12-a611-9991d192cc1e", + "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "635180f9-540f-5533-9d61-c5cfe14657fa", + "fd7ccb09-2768-5ceb-8b29-9b29cdef57a8", + "cc476583-54c8-5607-95bd-d06ae875dfb8" + ], + "contexts": [ + "review of polygenic risk scores for type 1 and type 2 diabetes. Int J Mol Sci. 2020;21(5):1703. 48. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:121924. 49. Ding Y, Hou K, Burch KS, Lapinska S, Priv F, Vilhjalmsson B, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "prediction of type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 45. Weedon, M. N. et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS. Med. 3, e374 (2006). 46. Euesden, J., Lewis, C. M. & OReilly, P . F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 14661468 (2015). 47. Gatineau, M. et al. Adult obesity and type 2 diabetes (Public Health England,", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "performance. Conclusions: By integrating T2D GWAS from multiple populations, we developed and validated a transancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare. Keywords: Polygenic risk score, Type 2 diabetes, Diverse populations, Clinical implementation", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "8.Padilla-Mart nez, F., Collin, F., Kwasniewski, M., and Kretow- ski, A. (2020). Systematic review of polygenic risk scores for type 1 and type 2 diabetes. Int. J. Mol. Sci. 21, 1703 . 9.Rao, A., and Knowles, J. (2019). Polygenic risk scores in coro- nary artery disease. Curr. Opin. Cardiol. 34, 435440 . 10.Dikilitas, O., Schaid, D.J., Kosel, M.L., Carroll, R.J., Chute, C.G., Denny, J.A., Fedotov, A., Feng, Q., Hakonarson, H., Jar-vik, G.P., et al. (2020). Predictive utility of polygenic risk scores", + "partitioned polygenic scores according to factors of disease heteroge- neity, as successfully demonstrated for type 2 diabetes (32). Another strategy could be the mapping of statistically associated genetic loci to different immune-cell subtypes according to gene expression patterns derived from single-cell RNA sequencing (33). Autoimmune PRS, possibly in combination with other genetic and nongenetic predictors, may be of importance to manage the risk of", + "genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated ( n= 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents ( n= 620), carotid intima-media thickness (cIMT) in adult women ( n= 781), dyslipidemia ( n= 7723), and coronary heart disease (CHD) ( n= 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. (Continued on next page)" + ], + "answer": "The interaction between multiple polygenic risk scores (PRS) can improve the prediction of Type 1 and Type 2 diabetes by combining information from various genetic loci associated with these diseases. This approach allows for a more comprehensive assessment of an individual's genetic risk. Specifically, combining information from common risk polymorphisms has been shown to improve disease prediction for Type 2 diabetes [3]. Additionally, partitioning polygenic scores according to factors of disease heterogeneity and mapping genetic loci to different immune-cell subtypes can enhance the predictive power of PRS, particularly for Type 2 diabetes [9]. These strategies leverage the aggregation of genetic risk from multiple sources, thereby capturing a larger proportion of the genetic variance underlying these traits and improving early diagnosis, intervention, and prevention efforts [4].", + "question": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_14 new file mode 100644 index 0000000..00ccf3c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2017 - Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf" + ], + "extraction_id": [ + "7f7a7f30-2e4e-50aa-bbcb-9f211c371e38", + "7a2a9981-4096-5049-a717-3e69eb609777", + "8bbfb009-87b7-54ae-8465-8796db8c271a", + "117cc1a5-d236-56b2-a69d-9c0a2fb9053d", + "dee54186-e75e-5ed2-818d-cd6f4370b153", + "7a2a9981-4096-5049-a717-3e69eb609777", + "10e4029f-0324-55c9-8fe8-023a924d1732", + "7a2a9981-4096-5049-a717-3e69eb609777", + "f740892a-7817-58b0-bec4-8648086b2353", + "65471d38-cd13-5de2-8c19-1eb72d24d6f5" + ], + "document_id": [ + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "6cf1eb8d-a91e-58a2-b6f4-29653678d0d3", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "afe53f5a-3962-520f-be55-9df5bfdaad70" + ], + "id": [ + "chatcmpl-AIHKFuXAocol6QH0B6QHJlkuJdiDC", + "b7812a7a-5504-57ca-8755-969dee45717e", + "d5c2a32a-b869-59c1-8a63-45ab620669de", + "ab373b7e-8c0b-59d8-9408-3e09ac76761e", + "a2adc65b-035b-568f-a0ae-9f7821ef45bc", + "887e1f7e-5044-5be8-a506-588ca7afa004", + "4bfcfbd6-f45e-553d-a043-a12e7abeff61", + "d32d6338-6cda-5f58-999d-2b4287ee4a77", + "ef0b8934-2af1-5848-88f9-ff5a2e4f3cc1", + "46ed97d7-7b3e-5be2-a409-04a37d105ef2", + "f06bcc81-6ef9-5874-8ef9-6bcb3c34b0d0" + ], + "contexts": [ + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse", + "therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome andRNA-sequencing data was employed to extensively characterize the genomic and molecularlandscape of insulinomas relative to normal beta cells. Here, we show at the pathway levelthat the majority of the insulinomas display mutations, copy number variants and/or dys-regulation of epigenetic modifying genes, most prominently in the polycomb and trithoraxfamilies. Importantly, these processes are coupled to co-expression", + "gesting that changes in alpha cell identity may ultimately lead to theirdysfunction. Analysis of normal and T2D islet single cells with simultaneous RNA-seq and patch clamping (patch-seq) also revealed subpopulations of alpha cells with varying enrichment for ER stressresponse genes (e.g., DDIT3, XBP1, PPP1R15A )[30]. Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets", + "RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. https ://doi.org/10.15252 /msb.20188 746 Ma L, Zheng J (2018) Single-cell gene expression analysis reveals -cell dysfunction and deficit mechanisms in type 2 diabe-tes. BMC Bioinform 19:515. https ://doi.org/10.1186/s1285 9-018-2519-1 Macaulay IC, Ponting CP, Voet T (2017) Single-cell multiom- ics: multiple measurements from single cells. Trends Genet 33:155168. https ://doi.org/10.1016/j.tig.2016.12.003", + "peak current. Prior single cell transcriptomic analyses have also notedsubpopulations of ER-stressed beta cells [31,32] which implicates the dysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to beReview S18MOLECULAR METABOLISM 27 (2019) S15 eS24/C2112019 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). www.molecularmetabolism.com", + "to understanding human development using single-cell tran-scriptomics. Development 144:1584. https ://doi.org/10.1242/dev.15045 8 Camp JG, Wollny D, Treutlein B (2018) Single-cell genomics to guide human stem cell and tissue engineering. Nat Methods 15:661667. https ://doi.org/10.1038/s4159 2-018-0113-0 Carrano AC, Mulas F, Zeng C, Sander M (2017) Interrogating islets in health and disease with single-cell technologies. Mol Metab 6:9911001. https ://doi.org/10.1016/j.molme t.2017.04.012", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been" + ], + "answer": "Recent single-cell RNA-sequencing studies have provided significant mechanistic insights into beta-cell failure pathways. These insights include: 1. **De-differentiation Signatures**: Single-cell analyses of human islet cells have revealed de-differentiation signatures, suggesting that beta cells may lose their specialized functions and revert to a more progenitor-like state, which contributes to their dysfunction in diabetes [1]. 2. **Transcriptional Regulation**: Advances in single-cell genomic profiling have enhanced our understanding of transcriptional regulation in non-beta cell types, which may play crucial roles in the hallmark features of beta-cell insufficiency and dysfunction in type 2 diabetes (T2D) [2]. 3. **ER Stress and Heterogeneity**: Single-cell transcriptomic analyses have identified subpopulations of beta cells experiencing endoplasmic reticulum (ER) stress. This stress is implicated in the dysfunction of both alpha and beta cells, contributing to diabetes pathogenesis [8]. These findings highlight the complexity of beta-cell failure and underscore the importance of single-cell technologies in unraveling the molecular mechanisms underlying diabetes.", + "question": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_15 new file mode 100644 index 0000000..80b5550 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2016 - NIH working group report using genomic information to guide weight management From universal.pdf", + "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf" + ], + "extraction_id": [ + "043ee0bf-ec42-57dd-aa0e-4f4f5aac2437", + "efbaf00f-0cb1-531f-a9fd-2844670ec92c", + "daf2d7fd-e789-5ceb-9984-d95656b5dd91", + "daf2d7fd-e789-5ceb-9984-d95656b5dd91", + "41ac576d-b850-5ee8-9753-ba9b060ba798", + "44d96546-84c3-51f1-85f9-22790a91d105", + "aff84b9e-3855-5960-accd-dcac6b362346", + "cbbe696b-8541-537a-ac5f-77b82cdb8201", + "68f800e3-8a0c-50ee-ba8b-ceb361f14fa5", + "9e010393-b98f-5f6c-a62d-fc0646ba8667" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "766edfd5-4756-51bf-b636-c94b041d030c", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "766edfd5-4756-51bf-b636-c94b041d030c", + "470f1f94-792d-5273-a88f-7e06084951c5", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "0ee28c8a-3618-559e-be0a-30f2579a0d1f", + "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2" + ], + "id": [ + "chatcmpl-AIHKMXGqZPQ6g67pYsMpeLJDEH7VZ", + "d198d71b-34ac-5402-b166-b476c15e2eef", + "d032460f-1bab-5710-8a79-872651f9c36e", + "a6efcd76-42f1-5d4c-bcfa-f5e63ac9eb4c", + "29bdfc8d-cb06-5ce5-8db6-f92b2f106b7d", + "cf4f3239-dd62-5eef-b5fc-85f4780e3f48", + "1c0b9205-340f-568f-990f-785c66154732", + "64f12ea8-a441-5fb5-a80e-1acbfb596a1e", + "8f447303-efd0-5564-9213-2cdc8fce12dd", + "7aafd7e0-2446-57a4-bb1e-1f77b4f4559e", + "cf775ed3-457e-52a7-b54b-5c1b5c179819" + ], + "contexts": [ + "To date, the overwhelming majority of studies including and assessing genetic variation have pro led the steady state patterns of epigeneticmodi cations and gene expression in islets or their constituent cell types. Others have compared how these steady state measures differ between T2D and non-diabetic (ND) individuals [13,16,40 e44]. Sur- prisingly, these studies, especially transcriptome analyses, haveidenti ed only modest alterations despite clear phenotypic differences", + "T1D and resulting complications (99). These epig enomic profiling studies suggest that, while a 415 reasonably stable histone methylation pattern is maintained in healthy individuals over time in a 416 cell-type specific setting, this pa ttern can be disrupted in a dis ease state. Moreover, they also 417 provide a glimpse of the inflammatory cell epig enome under the diabetic state and suggest that 418 new information about diabetes, its complicatio ns and metabolic memory can be obtained by 419", + "hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia. For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor- g(PPAR g) coactivator-1 agene (PPARGC1A) in diabetic islets ( Ling et al., 2008 ). Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients,", + "and correlated with mitochondrial content ( Barr /C18es et al., 2009 ). Epigenetic changes have also been suggested to be responsible forthe legacy effect of reduced risk of vascular complications after a period of sustained tight glucose control, or metabolic memory of transient hyperglycaemia and increased risk of diabetic vascular injury ( Pirola et al., 2010 ). Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had", + "of diabetes mellitus on the body is a high glucose stressed condition, altering substrate metabolism and causing systemic inflammation [60]. Due to this environmental change, researchers have shown how epigenetic changes occur across most, if not all, tissues that are impacted by diabetes mellitus [49, 61]. In the cardiovascular system, the heart, circulatory system, and regulating immune system are all tran -", + "nephropathy. Exp. Physiol. 98, 934945 (2013). 48. Reddy, M.A., Tak Park, J. & Natarajan, R. Epigenetic modifications in the pathogenesis ofdiabetic nephropathy. Semin. Nephrol. 33, 341353 (2013). 49. Li, S.L. etal. Enhanced proatherogenic responses in macrophages and vascular smooth muscle cells derived from diabetic db/db mice. Diabetes 55, 26112619 (2006). 50. El-Osta, A. etal. Transient high glucose causes persistent epigenetic changes and altered gene", + "exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fatoverfeeding. Diabetologia 2014;57:1154-1158. 53. Nilsson E, Jansson PA, Perfilyev A, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014;63:2962-2976. 54. Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies", + "etal. Hyperglycemia induces a dynamic cooperativity of histone methylase and demethylase enzymes associated with gene-activating epigenetic marks that coexist on the lysine tail. Diabetes (2009) 58:122936. doi:10.2337/ db08-1666 111. Keating S, Plutzky J, El-Osta A. Epigenetic changes in diabetic and cardio-vascular risk. Circ Res (2016) 118:170622. doi:10.1161/CIRCRESAHA. 116.306819 112. Paneni F, Volpe M, Lscher TF, Cosentino F. SIRT1, p66(Shc), and Set7/9 in" + ], + "answer": "The epigenetic landscape of key metabolic tissues shows several changes when comparing diabetic individuals to non-diabetic individuals: 1. **DNA Methylation Changes**: In diabetic individuals, increased DNA methylation has been observed in the promoter region of the PPARGC1A gene in both islets and skeletal muscle [3]. This suggests a potential mechanism by which gene expression related to metabolism is altered in diabetes. 2. **Histone Modifications**: There are disruptions in histone methylation patterns in diabetic states. While healthy individuals maintain stable histone methylation patterns, these can be disrupted in diabetes, indicating changes in the epigenome associated with inflammation and metabolic memory [2]. 3. **Impact on Gene Expression**: Epigenetic modifications, such as DNA methylation, have been linked to reduced expression of genes involved in diabetes and metabolism. Variations in DNA methylation have been noted near diabetes susceptibility genes and enhancers [6]. 4. **Tissue-Wide Epigenetic Changes**: Diabetes mellitus, characterized by high glucose stress, leads to epigenetic changes across most tissues impacted by the disease, including the cardiovascular system and immune system [7]. 5. **Adipose Tissue**: In subjects with type 2 diabetes, altered DNA methylation and differential expression of genes influencing metabolism and inflammation have been observed in adipose tissue [9]. These findings collectively suggest that diabetes is associated with specific epigenetic alterations across various metabolic tissues, which may contribute to the pathophysiology of the disease.", + "question": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_16 new file mode 100644 index 0000000..a953850 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2016 - Dissecting diabetes metabolic disease.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf" + ], + "extraction_id": [ + "57736895-897e-54e5-a735-aadcbd77cb63", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "5f8a0ddd-a0c7-5151-9b6a-e0980bb94aa6", + "0a3e3095-4789-505a-96b7-123a05078e95", + "a36cee80-5961-55e5-8ea4-8d4e1bc501a9" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "eee2f79d-e093-52fb-871a-798fd859235e", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6", + "51350055-d53c-5692-ab53-337b8a8bafd6" + ], + "id": [ + "chatcmpl-AIHKSpSdna9OyEUtDVRTMkIkEtBS5", + "f42c0f84-d2a8-5bf9-89c2-3dd182bfb235", + "2af36592-3e59-583c-a9c7-d612175f4afc", + "75b937b2-1e0b-5d63-b542-618ad91bbd1f", + "1f114642-3f77-5346-89e8-394c433f66ff", + "df30dab3-a490-5497-a079-2741f9039f87", + "eadf2320-de70-5499-ade0-7aa9930ac091", + "57b9550d-0258-5a87-be57-976f471e5763", + "1859f32b-8f5c-5c3c-9f4d-54193d37645d", + "99ccc9a2-865f-5d11-9b08-b26261d02fc9", + "83053df5-47ac-59da-9c30-69740a64372d" + ], + "contexts": [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "(276279). Through CRISPR-mediated HDR and base editing, it is possible to correct the vast majority of genetic variants, if notall. Conversion of GWAS-identi ed non-coding variants has not been conducted/documented in the diabetes eld, but it seems inevitable that such work will be carried out in the near futureHu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 11", + "Cas9 editing to restore insulin production in differentiated iPSCcells that mimicked neonatal diabetes ( 251,252). Likewise, Shi et al. converted a patient-speci c mutation in GATA6 gene and showed that the mutation involved (GATA6 R456C) has a similar effect to GATA6 knockout ( 21). Most recently, correction of a variant in the Wolfram syndrome 1 ( WFS1 ) gene by CRISPR- mediated HDR improved insulin secretion in iPSC-differentiatedb-like cells ( 253). Studies on GWAS identi ed genetic variants", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19" + ], + "answer": "Recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo include the use of CRISPR-mediated homology-directed repair (HDR) to correct specific genetic mutations associated with diabetes. For instance, CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method employed in iPSCs is CRISPR/Cas9-based HDR, where a Cas9-mediated cut is generated adjacent to the site of interest, and a homologous donor template with the intended nucleotide change is recombined by HDR [9]. Additionally, there has been a successful correction of a variant in the Wolfram syndrome 1 (WFS1) gene using CRISPR-mediated HDR, which improved insulin secretion in iPSC-differentiated beta-like cells [3]. These advancements highlight the potential of CRISPR-based genome editing to correct monogenic forms of diabetes by targeting specific genetic mutations in vivo.", + "question": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_17 new file mode 100644 index 0000000..3e4e96d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2020 - Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.pdf", + "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf", + "2012 - What will Diabetes Genomes Tell Us.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf" + ], + "extraction_id": [ + "978df5a8-acb4-53d3-b351-66a3bc613c78", + "aba850e8-8c0d-5256-b2ba-fa1dfc221114", + "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "3c30b33b-8928-5cee-9c37-c70642fff75c", + "2c601441-443d-5c47-95bb-6343378dd5dc", + "3dc37987-5204-5414-92ee-9d97af221261", + "50a110f8-e91d-5985-9fe9-62a373a58c9d", + "8dd91a24-2ac7-57b3-9cb3-f8ac74b1885c", + "f6926cab-e00d-5972-a815-2ecc9f8c35d5", + "9369222f-e125-58c0-8f2b-cf5daa867f77" + ], + "document_id": [ + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "8c310d76-0a3b-574c-9859-859258870ee5", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "5dd7d700-03db-595d-b1a5-beca77f9579e", + "fef1ae33-b3af-50ea-909c-f1b57f7fe981", + "38b3b7ab-d13e-5986-9a3a-54abe8a3e1e9", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f" + ], + "id": [ + "chatcmpl-AIHKYN37xsXdGCjQ8Ms8PgKZ10CIR", + "7302a27a-6e56-589d-a579-635f25fc46a3", + "4d780759-36bb-5295-a63a-16dab6aeab8c", + "ac4d8521-b492-59b5-9978-891f5a5ce0c5", + "81fb2df2-4154-58a7-b217-b07153a6c921", + "263ea999-9662-5518-a606-939f69d09f90", + "c807fc8b-966e-56a9-91ce-07b9baf940d9", + "ef027493-6063-5abd-9ee7-0c9a37379317", + "869d46b4-e379-54f8-bd71-143d9f31fa93", + "b92b959c-2f31-5177-8a21-627f3ee81b6c", + "7fd80e84-ec0c-564c-8e8b-278b8c622abb" + ], + "contexts": [ + "The integration of genetic, epigenetic, transcriptomic and phenotypic information allows to identify genes and novel metabolic pathway targets that deserve further attention to elucidate mechanistic relationships with insulin resistance and pancreatic islet failure. Although the GWASs and EWASs shed light onto (epi)genomic landscape of T2D to a great extent, these methods have still explicit limitations to conquer, such as sample size, small effect size, low allele frequency, genetic heterogeneity", + "map of the human genome, spurred larger multi-institutional programs (e.g., 1000 Genomes Projects, Encyclopedia of DNA Elements [ENCODE], and Roadmap Epigenomics), that have the goal of tracking genomic and epigenomic changes across multiple populations [ 8]. Aforementioned studies enabled GWASs for complex diseases such as T2D. DNA amplication, Sanger sequencing, and microarray studies have shed light on the genetics of diabetes but have only provided a limited amount of data. An", + "Abstract While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under- surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait", + "The advancement that has taken place in Genome-Wide Association Studies (GWAS) holds tremendous information related to various gene patterns associated with divergent illnesses that are complex and challenging to perform reductive analysis from a single locus, as stated by Cho Ys [6] and Coron [7]. The evolution of GWAS has focused on integrating data related to multi-locus across the gene that would assist in predicting complex illnesses", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "1. Introduction Genome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1]. Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide poly-", + "how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes. Keywords Genotyping .Genome-wide association . Sequencing .Imputation .Exome .Genome . Fine-mapping .Diabetes .Quantitative traits .Metabochip . Single nucleotide polymorphism Introduction GWA studies have made progress toward understanding the inherited basis of type 1 and type 2 diabetes by detecting disease-associated DNA variants, usually with allele fre-", + "complementary systems level data such as that related to protein- protein interactions and to and gene expression can provideinsights into the mechanisms underlying pathogenesis of complextraits [2224]. Here, we have combined these approaches towarddeciphering genome to phenome correlation in T2D ( Figure 1 ). Given that T2D GWAS genes do not directly relate to disease", + "phenotypes [2,6]. The recently accomplished deep sequencing of human exomes has indeed suggested that rare variations contribute substantially to human phenotypic variation and disease susceptibility [73]. Availability of post-GWASs era data for T2D will be crucial in examining genome to phenomecorrelation in greater details. Emerging methods in pathway-wide analysis and integrative network based analysis of genetic association data in complex disorders will further help accelerate", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories" + ], + "answer": "Genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes by combining genetic, epigenetic, transcriptomic, and phenotypic information. This integration helps identify genes and novel metabolic pathway targets that are crucial for understanding mechanistic relationships with insulin resistance and pancreatic islet failure [1]. Additionally, complementary systems-level data, such as protein-protein interactions and gene expression, provide insights into the mechanisms underlying the pathogenesis of complex traits like type 2 diabetes (T2D) [8]. This multi-omics approach allows for a more comprehensive understanding of the genome-to-phenome correlation in T2D, which is essential for examining the disease's complex genetic architecture [9].", + "question": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_18 new file mode 100644 index 0000000..384c7d7 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf", + "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf", + "2017 - Genome-wide DNA methylation variation in maternal and cord blood of gestational diabetes population.pdf", + "2010 - Autism Spectrum Disorders and Epigenetics.pdf", + "2017 - Genome-wide DNA methylation variation in maternal and cord blood of gestational diabetes population.pdf", + "2015 - Type 2 diabetes mellitus.pdf", + "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "2005 - Animal models of diabetes mellitus.pdf", + "2004 - Impaired glucose homeostasis in transgenic mice expressing the human transient neonatal diabetes mellitus locus.pdf", + "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf" + ], + "extraction_id": [ + "a9352adc-46d0-5947-a70d-940a7686008d", + "6ca1166c-ba51-5437-b325-5299e3e8fcef", + "971ff653-c42a-5366-ae2b-080df9aa679f", + "dcc77767-4641-5969-b3c1-4ea96a644a74", + "a17ed56f-20d4-56be-9aec-ac0b4943d19a", + "bbe952b1-6cc2-56a8-b5e8-5ca6b44b4316", + "e7e97f1e-d947-5b94-b2a9-5ac4b443628c", + "f7b36272-9780-52e8-9cb3-62d1c6c8c3b6", + "f68a90b3-5e03-57f4-8cb6-252e3a3fa132", + "a9352adc-46d0-5947-a70d-940a7686008d" + ], + "document_id": [ + "aa74b552-7e06-5596-8dec-298c40ad558c", + "3b301dd1-17bd-5632-9a96-d6294c6d7650", + "e02a2e19-3527-5466-b8d6-69e62f657698", + "6b435185-b16c-5b05-826b-eb98ca7bf806", + "e02a2e19-3527-5466-b8d6-69e62f657698", + "415516ba-5365-501b-84ce-0789045862f8", + "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "268bc8e3-7787-5bc0-8f7d-fffe20194dca", + "aa74b552-7e06-5596-8dec-298c40ad558c" + ], + "id": [ + "chatcmpl-AIHKdF53rZo0tRRSpImOeG4mHUbkt", + "10776283-4b6d-544c-89ac-0225c65bec1e", + "dc64e623-a130-5814-b54a-dd5f787f10d5", + "5495230d-c26d-5633-90e8-028912e5298a", + "4ecf5607-8d58-5908-aa1b-4416af202e69", + "a5412cf9-367c-518e-bb4f-77d8deb00a32", + "9814f4a0-2701-5920-bfd7-df5e1f3b134e", + "4f7b210f-26f7-5726-baff-8d469b2cc3df", + "8267bc80-1791-5e21-b228-053cba0629fd", + "4bb50efe-65b0-5c3c-9f58-03b423c93c0d", + "f703ae7e-5f64-52ee-860e-7b91b3066477" + ], + "contexts": [ + "maternal diabetes reduces the precision of gene regulation in exposed individuals. Loss of precision in embry-onic gene regulation may include changes to the epigenome via deregulated expression of chromatin-modify-ing factors. Unraveling the mechanisms underlying such epigenetic modications in diabetic pregnancies willhelp to understand how teratogenic insults compromise embryonic development and possibly provide ave-nues for therapeutic intervention. Birth Defects Research (Part A) 88:601611, 2010.", + "and metabolic imprinting: the ongoing effects of maternal hyper-glycemia. Diabetes Care 30:2287 2292 9. Clausen TD, Mathiesen ER, Hansen T et al (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women withgestational diabetes mellitus or type 1 diabetes: the role of intrauter- ine hyperglycemia. Diabetes Care 31:340 346 10. Solomon CG, Willett WC, Carey VJ et al (1997) A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA 278:1078 1083", + "M. Gestational diabetes alters offspring DNA methylation profiles in human and rat: Identification of key pathways involved in endocrine system disorders, insulin signaling, diabetes signaling, and ILK signaling. Endocriniology 2015;156:2222 -38. [33] Murphy SK, Huang Z, Hoyo C. Differentially methylated regions of imprinted genes in prenatal, perinatal and postnatal human tissues. PLOS ONE 2012;7:e40924.", + "12. Kim JK, Samaranayake M, Pradhan S. Epigenetic mechanisms in mammals. Cell Mol Life Sci. 2009;66:596-612. 13. Horsthemke B, Buiting K. Genomic imprinting and imprinting defects in humans. Adv Genet. 2008;61:225-246. 14. Iacobuzio-Donahue CA. Epigenetic Changes in Cancer. Annu Rev Pathol. 2009;4:229-249. 15. Temple IK. Imprinting in human disease with special reference to transient neonatal diabetes and Beckwith-Wiedemann syn- drome. Endocr Dev. 2007;12:113-123.", + "and Knowler W C. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study of discordant sibships. Diabetes 2000;49:2208 -11. [11] Feil R and Fraga MF. Epigenetics and the environment: Emerging patterns and implications. Nature Reviews Genetics 2012;13:97 -109. [12] Recillas -Targa F. DNA Methylation, Chromatin boundaries, and mechanisms of genomic imprinting. Archives of Medical Research 2002;33:428 -38.", + "53. T ravers,M.E. etal. Insights into the molecular mechanism for type2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62, 987992 (2013). 54. Gulli,G., Ferrannini,E., Stern,M., Haffner,S. &DeFronzo,R.A. The metabolic profile of NIDDM isfully established in glucose-tolerant offspring of twoMexican-American NIDDM parents. Diabetes 41, 15751586 (1992). PRIMER NATURE REVIEWS | DISEASE PRIMERS VOLUME 1 | 2015 | 17", + "Gaudet, D., Hivert, M.F., Brisson, D., Bouchard, L., 2013 Sep. Gestational diabetesmellitus epigenetically affects genes predominantly involved in metabolic dis- eases. Epigenetics 8 (9), 935 e943. Salbaum, J.M., Kappen, C., 2012 Oct. Responses of the embryonic epigenome to maternal diabetes. Birth Defects Res. A Clin. Mol. Teratol. 94 (10), 770 e781. Salbe, A.D., Lindsay, R.S., Collins, C.B., Tataranni, P.A., Krakoff, J., Bunt, J.C., 2007 Feb.", + "environment are probably mediated by a permanent program-ming of the developing offspring, e.g. by the mechanism ofimprinting. Of interest, the increased risk of diabetes continuesinto subsequent generations, suggesting the changes also affectthe germ cell line [143]. Conclusions There is little doubt that some animal models of diabetes have", + "tal diabetes and later onset diabetes: a case of inher - ited insulin resistance. Arch. Dis. Child. 72:5657. 6. Temple, I.K., et al. 1995. An imprinted gene(s) for diabetes? Nat. Genet. 9:110112. 7. Temple, I.K., et al. 1996. Further evidence for an imprinted gene for neonatal diabetes localised to chro -", + "1994; Martinez-Frias et al., 1998). The underlying mecha-nisms are not well understood, but are thought to involve various responses of the embryonic genome to the adverse intrauterine environment (Greene, 2001;Loeken, 2008). To explore how conditions of maternal diabetes affect gene expression in the embryo, we recently conducted expression proling experiments on embryos from dia-betic dams compared to embryos from normal dams(Pavlinkova et al., 2009). We were able to demonstrate" + ], + "answer": "Genomic imprinting has a significant impact on the susceptibility and progression of diabetes. Imprinting can influence the expression of genes involved in metabolic processes, which are crucial in the development of diabetes. For instance, changes in imprinting status at specific loci, such as the KCNQ1 locus, have been linked to type 2 diabetes susceptibility, indicating that temporal changes in imprinting can affect the function of pancreatic islets and contribute to diabetes risk [6]. Additionally, imprinting defects have been associated with specific forms of diabetes, such as transient neonatal diabetes, suggesting that imprinted genes play a role in the disease's onset and progression [4]. Furthermore, the effects of maternal diabetes on the offspring's epigenome, including alterations in DNA methylation profiles, highlight the role of imprinting in the intergenerational transmission of diabetes risk [3], [7]. These epigenetic changes can lead to a permanent programming of the developing offspring, increasing the risk of diabetes in subsequent generations [8]. Overall, genomic imprinting is a critical factor in understanding the genetic and epigenetic mechanisms underlying diabetes susceptibility and progression.", + "question": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_19 new file mode 100644 index 0000000..7f5c70b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2012 - The Genetic and Epigenetic Basis of Type 2 Diabetes and Obesity.pdf", + "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf" + ], + "extraction_id": [ + "3bf3c6a7-de03-5114-bad8-d53fd76d0fba", + "08acfe03-73b3-5533-b8e4-9caa031d33dd", + "cfc4760c-755e-5693-8d7b-4332fb6c45e5", + "50bde36d-2968-5eaa-9713-924e73383427", + "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "512ae4b5-27c8-509c-87ad-abd64d4295a6", + "df2a8699-692f-5f25-94b3-508f9ed2f210", + "c362793d-c70f-5225-afe5-88098042daef", + "08acfe03-73b3-5533-b8e4-9caa031d33dd", + "232f9536-eeac-5739-a57d-770cf5b32947" + ], + "document_id": [ + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "d74ac751-712b-5970-98e6-bd348adc1dee", + "8c310d76-0a3b-574c-9859-859258870ee5", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ce4f171c-494c-53f2-a770-c3edd3561c40" + ], + "id": [ + "chatcmpl-AIHKkTED9VE0du8urGhS0MeefXMR7", + "ee24ad01-f93a-55c4-8c2c-9dea6a6a84d5", + "de2af111-7fad-5dc1-baae-4742ccc8ba0d", + "e07d8080-aba7-5216-8a75-e078201b8c0a", + "e76c1d0c-33b7-5d9e-958f-fce6adfe81aa", + "30728ec3-882c-5bb0-8f41-4c74dfafdf13", + "f7ed49ac-f617-5c13-851e-98d1583e020f", + "151c185f-3300-5518-810c-3fb0d6715f2c", + "cc98a5b9-131e-5b60-919e-82e86b7a37a7", + "a94c609e-4816-5e10-96fd-ba8d79218405", + "1d13cf78-3215-5873-b910-cbcac141779b" + ], + "contexts": [ + "genome-wide association scans on type 2 dia-betes (Lango et al, 2008 ; van Hoek et al, 2008 ). Both studies found a similar predictive value showing only a marginal improvement in the prediction of type 2 diabetes beyond classicalclinical characteristics. Thus, despite overwhelming signicances and repeated replications, the explained variance andpredictive value of the currently identied sus- ceptibility loci is too low to be clinically useful. 5 GeneEnvironment Interactions in Obesity and Diabetes", + "actions between genetic variation and environmental exposures and medical therapies has important implications for the predic- tion, targeted prevention, and s tratified treatment of T2D and many other diseases. The literature on gene-e nvironment interactions in diabetes-related traits is extensive, but few studies are accom- panied by adequate replication data or compelling mechanistic explanations. Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be", + "ined for a range of disorders, from diabetes, cancer and in ammatory bowel disease to depression. We refute the contention that incorporating the measurement of genotype into longitudinal-epidemiological studies is wasteful or unlikely to yield signi cant bene ts. 2008 Genetic effects on environmental vulnerability to disease. Wiley, Chichester (Novartis Foundation Symposium) p 128142 Slow progress understanding the genetic basis of many common diseases has been", + "In principle, each of these loci provides an opportunity to define the genetic architecture and pathophysiology of these traits. The earliest successes for genetic discovery in diabetes and obesity arose from the study of monogenic and syndromic forms of disease, for which the segregation of rare, but highly penetrant, alleles could be tracked using family-based linkage approaches that are well suited to that setting. Maturity-onset diabetes of the young, for example, accounts for ~12% of cases", + "wide GxE interactions in explaining the variance of diabetes-related traits. Citation: Zheng J-S, Arnett DK, Lee Y-C, Shen J, Parnell LD, et al. (2013) Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits. PLoS ONE 8(10): e77442. doi:10.1371/journal.pone.0077442 Editor: Maria Eugenia Saez, CAEBi, Spain Received April 10, 2013; Accepted September 3, 2013; Published October 28, 2013", + "data sharing to advance complex disease research. Nat. Rev. Genet. 17, 535549 (2016). 82. Franks,P .W., Pearson,E. & Florez,J.C. Gene- environment and gene-treatment interactions in type2 diabetes: progress, pitfalls, and prospects. Diabetes Care 36, 14131421 (2013). 83. Hagberg,J.M., Jenkins,N.T . & Spangenburg,E. Exercise training, genetics and type2 diabetes- related phenotypes. Acta Physiol. 205, 456471 (2012). 84. Langenberg,C. etal. Gene-lifestyle interaction and", + "Genomics and geneenvironment interactions Even though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mel - litus are more susceptible to T2DM than others, which suggests that individual differences in response to life - style interventions exist76. Substantial evidence from twin and family studies has suggested a genetic basis of T2DM77. Over the past decade, successive waves of", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "fuel subsequent functional and clinical translation studies. This is important, because diabetes medicine may rely increas- ingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative. How Are Gene-Environment Interactions Defined? The term gene-environment interaction has different meanings to different biomedical re searchers (see Supplement 1for glossary of terms used). However, here, we focus on the", + "Nutrients 2014, 6 5362 48. Cornelis, M.C.; Hu, F.B. Gene -enviroment interactions in the development of type 2 diabetes: Recent progress and continuing challenges. Annu. Rev. Nutr. 2012, 32, 245259. 49. Lee, Y.C.; Lai, C.Q.; Ordovas, J.M.; Parnell, L.D. A database of gene -enviroment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes. J. Data Mining Genomics Proteomics 2011, 2, 106, doi:10.4172/2153- 0602.1000106." + ], + "answer": "Longitudinal genomics studies are crucial for understanding gene-environment interactions in diabetes onset and management because they allow researchers to observe how genetic variations interact with environmental factors over time. This approach helps in identifying temporal patterns and causal relationships that are not possible to discern from cross-sectional studies [2]. By incorporating genotype measurements into longitudinal studies, researchers can gain significant insights into the genetic basis of diseases like diabetes, which can lead to better prediction, targeted prevention, and stratified treatment of type 2 diabetes (T2D) [3]. Additionally, these studies can help stratify T2D into subclasses that can be treated more effectively by understanding gene-lifestyle interactions [8]. Overall, longitudinal genomics studies provide a comprehensive framework to explore how genetic predispositions and environmental exposures collectively influence diabetes-related traits and disease progression.", + "question": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_2 new file mode 100644 index 0000000..6e349ba --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "2010 - Liver and Adipose Expression Associated SNPs.pdf", + "2016 - Transcriptomics in type 2 diabetes Bridging the gap between genotype and phenotype.pdf", + "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "2012 - Finding Genetic Risk Factors of Gestational Diabetes.pdf", + "2015 - Genetic Studies on Diabetic Microvascular Complications.pdf" + ], + "extraction_id": [ + "5f148509-8a55-5e9c-8c68-e327f519c1c9", + "692b342f-5d48-5046-84f9-37f1cf4275b5", + "d7e0e5ad-bad5-5b14-896e-45702d6605f9", + "a620eedf-5d5b-506f-97f5-c25dbe0493c0", + "1213249d-8ed3-5d13-9137-f11b87a7a78b", + "35ce49d5-7af3-5f24-927c-f800e8ae024d", + "71934c29-338d-57a2-8f45-e3e795e0ec9b", + "924d35c5-0ee8-53a7-9fdf-9309a27ce9ae", + "e7bf3f2d-8180-5a84-965c-8289f107a718", + "d3335459-5fec-5104-932f-f4fd7566edf7" + ], + "document_id": [ + "51350055-d53c-5692-ab53-337b8a8bafd6", + "368e0215-393e-5bec-a87c-e976adaa3ca5", + "b9194555-5fdb-549e-9edb-d108132a7dd1", + "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "7d051350-d939-5183-be22-742727573a75", + "ebeef1bf-341d-5aa1-807b-1f23186cf2bc", + "98e49a13-9887-5b27-879b-0816a3da1c1d", + "7d051350-d939-5183-be22-742727573a75", + "81d6ccba-6203-5879-b206-b8711d1ff35c", + "1df9d9a8-0fb0-5a03-9749-9471b4b2b2f3" + ], + "id": [ + "chatcmpl-AIHIcyJRqSPUlYLtzZ5hVN5aLL9iw", + "0c0634ba-c437-52d3-b3a9-caa5eda120c6", + "1ab64c6e-e930-597e-bc12-ed540eabcf46", + "46ac5572-ac56-5f29-b7bf-49a1e29d3936", + "6d5d4c24-5bc8-539a-9faa-8b2370f8c87a", + "54da57b3-e577-5c00-a7d5-6f569a41d28b", + "0cf52952-0d83-58ed-b402-05dd2f085841", + "2a91a466-c271-5368-b0a1-cf15e6478bb1", + "de3b49f1-9dcc-5056-8232-b76e5f985736", + "72622bca-2fce-5732-9c8b-2909d231d09d", + "5af0c2b9-9957-5c8f-b8ae-c115e365576f" + ], + "contexts": [ + "wide association study identi es novel risk loci for type 2 diabetes. Nature (2007) 445:881 5. doi: 10.1038/nature05616 27. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science (2007) 316:1341 5. doi: 10.1126/science.1142382 28. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature (2016) 536:41 7.", + "novel loci for type 1 diabetes. Diabetes 58:290295. DOI: https://doi.org/10.2337/db08-1022, PMID: 18840781 Huang J, Ellinghaus D, Franke A, Howie B, Li Y . 2012. 1000 Genomes- based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data. European Journal of Human Genetics 20:801805. DOI: https://doi.org/10.1038/ejhg.2012.3, PMID: 22293688 Hundhausen C, Roth A, Whalen E, Chen J, Schneider A, Long SA, Wei S, Rawlings R, Kinsman M, Evanko SP ,", + "general population, these loci show limited effect in DKD, especially in individuals with type 1 diabetes [ 6]. Genome- wide association studies (GWAS) have previously identified ahandful of genetic loci for DKD at the genome-wide signifi- cance level ( p<510 8)[711]. Recently, a meta-analysis of GWAS, including up to 19,406 individuals with type 1 diabetes from the Diabetic Nephropathy Collaborative Research", + "Table 2.1 Major published T2D GWAS and meta-analyses StudyEthnicity/ origin NcasesaN controlsaNovel loci identiedGWAS or meta-analysis discoveryapproach GWAS arrayReference panel forimputationT2D phenotype denition/otherspecs Diabetes Gene Discovery Group (Sladek et al. 2007 ), NatureEuropean 694 645 SLC30A8 ,HHEX /IDE GWA Illumina 300k + Family history of T2D, AAO <45 years, BMI <30 kg/m 2 FinlandUS Investi-gation of NIDDMGenetics (FUSION)(Scott et al. 2007a ), ScienceEuropean 1161 1174 CDKN2A/2B ,", + "scale gene-centric meta-analysis across 39 studies identifies type 2diabetes loci. Am J Hum Genet. 2012;90(3):410 25. 13. Haiman C, Fesinmeyer M, Spencer K, Buzkova P, V oruganti V , Wan P, et al. Consistent directions ofeffect for established type 2 diabetes risk variants across populations: the Population Architectureusing Genomics and Epidemiology (PAGE) Consortium. Diabetes. 2012;61(6):1642 7.In the most complete trans-ethnic T2D GWAS", + "9. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881885. 10. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta- analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638645.11. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322: 881888.", + "scale ongoing efforts to localize and characterize T2D susceptibility genes using genome-wide association study (GWAS) approaches. To date, the GWAS method has achieved substantial success in localizing novel T2D susceptibility loci and loci for T2D-related glycemic traits (about 90 loci), obesity loci (~90), and loci for metabolic syndrome or its components (~50 loci), e.g. reviews: [4,20,28,29,41,47,51,64,65,67] . However, common variants identi ed by GWAS explain only about", + "T2D GWA meta-analysis performed by the DIAbetes Genet-ics Replication and Meta-analysis (DIAGRAM) Consortium [6]. Using genotype data from almost 150,000 individuals, predominantly of European descent, the consortium was ableto define 10 novel T2D-susceptibility loci to genome-wide significance, and to highlight several hundreds more that, whilst failing to reach the stringent criteria typically regardedas proof, are nonetheless highly likely to reflect genuine", + "18. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-885. 19. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007; 316:1341-1345. 20. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT , Lund University, and Novartis Institutes of BioMedical", + "additive, dominant, and recessive) and did not adjust for mul - tiple comparisons. The third study is the largest GWAS con - ducted to date and is a meta-analysis of two GWASs, Genetics of Kidneys in Diabetes (GoKinD) and Epidemiology of Dia - betes Interventions and Complications (EDIC) studies [24]. This study by Grassi et al. [24] involved 2,829 European sub - jects with T1DM. The most significant variant was rs476141 located in a long non-coding RNA ( LOC339529 ) in chromo -" + ], + "answer": "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified several novel loci associated with diabetes. Specifically, the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, using genotype data from almost 150,000 individuals predominantly of European descent, identified 10 novel type 2 diabetes (T2D) susceptibility loci that reached genome-wide significance [8].", + "question": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_20 new file mode 100644 index 0000000..5c744cb --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2017 - Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf" + ], + "extraction_id": [ + "7a2a9981-4096-5049-a717-3e69eb609777", + "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "65471d38-cd13-5de2-8c19-1eb72d24d6f5", + "7f7a7f30-2e4e-50aa-bbcb-9f211c371e38", + "8bbfb009-87b7-54ae-8465-8796db8c271a", + "bdf327a6-decb-5c7a-a981-a7969206b455", + "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "312b1856-e1b1-5ae7-8cba-370becf5f7cb", + "117cc1a5-d236-56b2-a69d-9c0a2fb9053d" + ], + "document_id": [ + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "6cf1eb8d-a91e-58a2-b6f4-29653678d0d3", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "470f1f94-792d-5273-a88f-7e06084951c5", + "b9bc63a5-e366-5685-bd7a-4732a8eeffb7" + ], + "id": [ + "chatcmpl-AIHKoCrJvacxorigznvNb5BV4LGGI", + "d5c2a32a-b869-59c1-8a63-45ab620669de", + "1c659cb4-085b-55b9-be3c-6332c36cbeba", + "f06bcc81-6ef9-5874-8ef9-6bcb3c34b0d0", + "b7812a7a-5504-57ca-8755-969dee45717e", + "ab373b7e-8c0b-59d8-9408-3e09ac76761e", + "7a5c8fad-97c5-59d2-8e5e-ee72d3dc2362", + "b7c1d2be-88c5-5f33-b812-b05e842f1647", + "11a5527b-8d22-5e69-8a84-6d9180517d81", + "db06230d-31c0-5947-8c1c-f58c48b6f439", + "a2adc65b-035b-568f-a0ae-9f7821ef45bc" + ], + "contexts": [ + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "Genes 2018 ,9, 374 7 of 19 4. Single-Cell RNA-seq as a Novel Approach in High-Throughput Type 2 Diabetes Research Islets of Langerhans are heterogeneous structures that consist of different cell types. Further research is needed to track genetic changes in individual pancreatic islet cells and in sorted cell populations. The massive development of NGS allowed the sequencing of single cells from human pancreatic islets. Considering the cell-type heterogeneity within Langerhans islets, such an approach", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been", + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "24. Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res. 23, 1554 1562 (2013). 25. Takane, K. K., Bender, A. & Stewart, A. F. Speci c targeting and sorting of puried human beta cells: de ning the human beta cell transcriptome. ADA Scienti c Sessions, San Francisco (2014). 26. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).", + "5. Genome-Wide Proling of Epigenetic Changes in Pancreatic Islets and Peripheral Tissues Epigenetic data added another layer of complexity to our understanding of the genomic bases of T2D. Given that a variable epigenetic pattern can modulate the link between the SNP and trait, consideration of this interplay is critically important. Molecular epigenetics involves changes in gene function that occur without a change in the nucleotide sequence via DNA methylation, histone", + "and model organisms. The combination of data from high-throughput approaches and association studies has provided compelling evidence that some epigenetic markers contribute to the risk of T2D [ 57,58]. Epigenetic alterations have been shown to affect the expression of genes that are crucial for maintaining pancreatic islet secretory capacity, survival, and functional identity and the proper response to insulin in peripheral tissues [ 59,60]. Furthermore, several epigenetic signatures, such", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse" + ], + "answer": "Recent integrative genomics approaches, such as the combination of single-cell RNA sequencing and epigenomic profiling, have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. These approaches have allowed researchers to delve deeper into the transcriptional regulation of non-beta cell types, which may play crucial roles in the hallmark features of beta cell insufficiency and dysfunction associated with Type 2 Diabetes (T2D) [1]. Single-cell RNA sequencing has been particularly instrumental in high-throughput diabetes research by enabling the sequencing of individual cells from human pancreatic islets. This is important given the heterogeneity within the islets of Langerhans, which consist of various cell types. By tracking genetic changes in individual cells, researchers can better understand the complex cellular landscape and the specific contributions of different cell types to diabetes pathogenesis [2]. Furthermore, epigenomic profiling adds another layer of complexity by revealing how epigenetic changes can modulate gene expression without altering the DNA sequence. These changes are crucial for maintaining the secretory capacity, survival, and functional identity of pancreatic islets, as well as their response to insulin [8]. The integration of these genomic and epigenomic data helps identify regulatory elements and pathways that could be targeted for therapeutic interventions, moving from correlation to causation in understanding diabetes [10].", + "question": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_3 new file mode 100644 index 0000000..0e78189 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2013 - Epigenetic Modifications in the Pathogenesis of Diabetic Nephropathy.pdf", + "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "2016 - Epigenomic profiling reveals an association betweenpersistence of DNA methylation and metabolicmemory in the DCCTEDIC type 1 diabetes cohor.pdf", + "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf" + ], + "extraction_id": [ + "77eb6a3d-2e3b-5304-873f-4fe14ec290d1", + "21de4c95-4171-52bb-a867-2df5336c3c71", + "3d7cb780-5f0a-5500-8176-4c2055cac9dc", + "77eb6a3d-2e3b-5304-873f-4fe14ec290d1", + "cbbe696b-8541-537a-ac5f-77b82cdb8201", + "7680731d-0b98-5f45-85f9-d06883504dd1", + "767d65c7-b99d-5427-8f5a-4afa10669e11", + "7a924f08-78ef-528a-8f9e-7bc12b004ff2", + "745c11f0-789f-5f0a-9f19-69af42a19c75", + "44d96546-84c3-51f1-85f9-22790a91d105" + ], + "document_id": [ + "766edfd5-4756-51bf-b636-c94b041d030c", + "766edfd5-4756-51bf-b636-c94b041d030c", + "70945353-4808-539a-80f9-5632c27913e5", + "766edfd5-4756-51bf-b636-c94b041d030c", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "9cffb997-a205-5f72-89a6-945df5b9af28", + "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "4b44425c-00c2-504f-be3c-34c002951cc2", + "470f1f94-792d-5273-a88f-7e06084951c5" + ], + "id": [ + "chatcmpl-AIHIljZhr1AUuC7qfsdHZaKkRKz2A", + "eb133825-7500-5160-b39a-298961323f9c", + "a97f140f-63b1-5963-9c38-d90f59f58ced", + "41899c3d-64db-556a-882a-4e39b964c6d5", + "6f647f65-0c70-5abf-8944-e2b1ade8ee1d", + "883de652-2a30-5587-89bb-474facc861fe", + "796ed77e-4539-543b-a392-5736392f93ba", + "3f3fb648-0a87-5d2b-82c8-da1f3caf91b0", + "aaeb4ad0-7848-554e-8ec1-2b5a094d3112", + "c51c94d1-c182-5e77-8a14-6af868d66ee1", + "1c0b9205-340f-568f-990f-785c66154732" + ], + "contexts": [ + "diabetes due to epigenetic silencing of Pdx1, a key transcription factor that regulates insulin gene 301 expression and beta cell differentiation. Both hi stone modifications a nd DNA methylation were 302 implicated (111). In another study, it was shown th at, in diabetic islets , there was increased DNA 303 methylation of the promoter of PPAR-gamma co-activator 1 gene ( PPARGC1A ), a factor that 304 plays a key role in regulating mitochondrial ge nes and in the modulation of diabetes (87). 305", + "altered DNA methylation (DNA-me) at various genes in target cells all of which over time can 1009 result in changes to the expr ession patterns of inflammatory, sclerotic and other pathological 1010 genes and the ultimate developm ent of diabetic complications. 1011 1012 Figure 2: Model for epigenetic regulation of pa thological gene expressi on in diabetes via 1013 changes in chromatin histone modifications. Post translational modifications on the N- 1014", + "Dependent Demethylation of Regulatory Elements Correlates with Chromatin State and Improved Cell Function. Cell Metab. 2015 ,22, 619632. [CrossRef] 228. Zhang, H.; Pollin, T.I. Epigenetics Variation and Pathogenesis in Diabetes. Curr. Diab. Rep. 2018 ,18, 121. [CrossRef] 229. Miao, F.; Chen, Z.; Zhang, L.; Liu, Z.; Wu, X.; Yuan, Y.-C.; Natarajan, R. Proles of epigenetic histone post-translational modications at type 1 diabetes susceptible genes. J. Biol. Chem. 2012 ,287, 1633516345. [CrossRef]", + "Epigenetic Mechanisms in Diabetic Complications 14 DNA methylation at prom oter CpG islands has been associ ated with gene repression and 292 is a well studied epigenetic mark in the c ontext of tumor suppressor genes and cancer (129). 293 However, much less is known a bout DNA methylation in diabetes . A recent report has shown 294 that the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes 295", + "Epigenetics: deciphering its role in diabetes and its chronic complications. Clin. Exp. Pharmacol. Physiol. 38, 401409 (2011). 61. Cooper, M.E. & El-Osta, A. Epigenetics: mechanisms and implications for diabetic complications. Circ. Res. 107, 14031413 (2010). 62. Miao, F. etal. Profiles of epigenetic histone post- translational modifications at type1 diabetes susceptible genes. J.Biol. Chem. 287, 1633516345 (2012). 63. Sapienza, C. etal. DNA methylation profiling", + "Emerging evidence shows that epigenetic mecha-nisms in chromatin including histone PTMs, DNAme, and miRNAs also might play key roles in the etiology of diabetes and DN. The persistence ofepigenetic modi cations triggered by diabetic stim- uli could be one of the key mechanisms underlying metabolic memory. A role for several HMTs and thecorresponding histone PTMs has been shown in the expression of brotic and in ammatory genes asso-", + "inflammation-related epigenetic modifications: focus on DNA methylation. Exerc Immunol Rev. 2015;21:26 41. 17. Milagro FI, Mansego ML, De Miguel C, Martinez JA. Dietary factors, epigenetic modifications and obesity outcomes: progresses and perspectives. Mol Aspects Med. 2013;34(4):782 812. 18. Caramori ML, Kim Y , Goldfine AB, et al. Differential gene expres- sion in diabetic nephropathy in individuals with type 1 diabetes. J Clin Endocrinol Metab. 2015;100(6):E876 82.", + "elevated glucose level is not the only factor that leads to mal- adaptive epigenetic modifications in diabetes. DNA methyla- tion can also be influenced by reactive oxygen species, both directly through oxidative m odification DNA preventing methylation and indirectly through its effects on methylation writing/erasing enzymes [ 15]. Many other factors including hypoxia, inflammation, cytokines and growth factors, drugs, nutrition and even physical activity can modify epigenetic", + "1306 1313. 31. Miao F, et al.; DCCT/EDIC Research Group (2014) Evaluating the role of epigenetic histone modifications in the metabolic memory of type 1 diabetes. Diabetes 63(5): 1748 1762. 32. Reddy MA, Tak Park J, Natarajan R (2013) Epigenetic modifications in the patho- genesis of diabetic nephropathy. Semin Nephrol 33(4):341 353. 33. Bell CG, et al. (2010) Genome-wide DNA methylation analysis for diabetic nephrop- athy in type 1 diabetes mellitus. BMC Med Genomics 3:33.", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had" + ], + "answer": "Epigenetic modifications, such as DNA methylation and histone modification, play significant roles in influencing the expression of diabetes-related genes. 1. **DNA Methylation**: This modification can lead to the silencing of key genes involved in diabetes. For example, increased DNA methylation of the promoter of the PPARGC1A gene, which is crucial for regulating mitochondrial genes, has been observed in diabetic islets. This methylation can modulate diabetes by altering gene expression [1]. Additionally, altered DNA methylation patterns can change the expression of inflammatory and pathological genes, contributing to the development of diabetic complications [2]. 2. **Histone Modifications**: These modifications can affect chromatin structure and gene expression. In the context of diabetes, histone post-translational modifications (PTMs) have been associated with the regulation of genes susceptible to type 1 diabetes [3]. Changes in chromatin histone modifications can regulate pathological gene expression in diabetes, influencing the disease's progression [2]. Overall, both DNA methylation and histone modifications can lead to the repression or activation of genes that are critical in the pathogenesis and progression of diabetes, affecting insulin production, inflammation, and other metabolic processes.", + "question": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_4 new file mode 100644 index 0000000..d8dc230 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 - The intestinal microbiome in type 1 diabetes.pdf", + "2010 - Gut Microbiota in Human Adults with Type 2 Diabetes.pdf", + "2014 - Diabetes in Europe An update.pdf", + "2016 - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.pdf", + "2012 - A metagenome-wide association study of gut microbiota in type 2 diabetes.pdf", + "2014 - Microbiota and diabetes an evolving relationship.pdf", + "2014 - Microbiota and diabetes an evolving relationship.pdf", + "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "2019 - Systems genetics approaches to probe gene function.pdf" + ], + "extraction_id": [ + "b5ecc19b-eae0-51de-8e87-e5d01060e5be", + "0eb4bb40-b16c-5203-8c83-dac0695d43a2", + "5c27f434-3a7c-5ec9-80fc-6399dd3570c3", + "092a9b75-9985-5876-a650-59bc3f0d10fb", + "0a4d545f-0682-5ce1-b38c-88b5fdb4add3", + "44b12386-be75-5141-a5a0-77ab97136863", + "223f3f31-fb62-5f0d-ac8a-5a6deb1191d2", + "3754ce7f-9671-5636-a4e6-849fb672366a", + "736476e2-62be-52c5-b4a2-ee7cd7666a6f", + "5ab39f63-c4e0-56b8-b6ed-26df7bee89af" + ], + "document_id": [ + "138189d1-a16e-5c76-9b19-bd6877e7ee6d", + "27aaf82e-944d-55b3-8b6d-cc43bcdb3eab", + "81e1fc53-6768-590f-9b47-9a5105b6ddb5", + "f0405966-38bf-5a04-aa2c-1474b11362bb", + "0c088ef3-83a7-5a5e-8308-011cf4b25924", + "4bbbe579-1d9e-50b8-9403-b50bc3282c8f", + "4bbbe579-1d9e-50b8-9403-b50bc3282c8f", + "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683" + ], + "id": [ + "chatcmpl-AIHItZX0vwpceBtjbHWMD13xwSdHl", + "d79a5c86-df6a-5b3d-93b4-a26f47b47e83", + "6cef232c-d7c6-5968-ad74-2903b688793a", + "89360f80-d048-5c02-a61d-6d56a99eedcd", + "e7e8ef7b-bad0-54bc-814d-d947ea04756b", + "da881999-9d70-560f-91b3-eda465b7a639", + "2589b0db-190e-5847-aef0-0bc3b415fb94", + "a5d5d05b-a824-5b8f-a774-b0b9ec5d0182", + "63e887b3-0db0-547d-a81c-716909ead0b6", + "d9bc6a49-c40e-520f-9e2d-afa05829416f", + "b0aa9c89-a8f4-5388-97ed-5d6556c565e7" + ], + "contexts": [ + "diabetes? Is altered gut epithelial function and integrity important in the pathoge nesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to dysbiosis and how do we demonstrate impaired function in humans? How important are the interactions between host genetics, metab olism and the immune system in shaping the microbiome and predilection to disease?", + "the gut, which might trigger an inflammatory response and play arole in the development of diabetes. In conclusion, our data suggest that the levels of glucose tolerance or severity of diabetes should be considered while linking microbiota with obesity and other metabolic diseases in humans. It is especially important for developing the strategies to modify the gut microbiota inorder to control metabolic diseases, since obesity and diabetes mightbe associated with different bacterial populations. Methods", + "2011;342:d35. [68] Hara N, Alkanani AK, Ir D, Robertson CE, Wagner BD, Frank DN, et al. The role of the intestinal microbiota in type 1 diabetes. Clin Immunol 2013;146:1129. [69] Beyan H, Wen L, Leslie RD. Guts, germs, and meals: the origin of type 1 diabetes. Curr Diab Rep 2012;12:45662. [70] Atkinson MA, Chervonsky A. Does the gut microbiota have a role in type 1 diabetes? Early evidence from humans and", + "diabetes. ISME J. 5,8291 (2011). 30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011). 31. Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014). 32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260273 (2015).", + "661678 (2007). 4. Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 13411345 (2007). 5. Musso, G., Gambino, R. & Cassader, M. Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu. Rev. Med. 62, 361380 (2011). 6. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 16351638 (2005).", + "The gut microbiota affects numerous biological functionsthroughout the body and its characterisation has becomea major research area in biomedicine. Recent studieshave suggested that gut bacteria play a fundamental rolein diseases such as obesity, diabetes and cardiovasculardisease. Data are accumulating in animal models andhumans suggesting that obesity and type 2 diabetes(T2D) are associated with a profound dysbiosis. Firsthuman metagenome-wide association studiesdemonstrated highly signi cant", + "18 Burcelin R. Regulation of metabolism: a cross talk between gut microbiota and its human host. Physiology (Bethesda) 2012;27:300 7. 19 Breen DM, Rasmussen BA, Cote CD, et al . Nutrient-sensing mechanisms in the gut as therapeutic targets for diabetes. Diabetes 2013;62:3005 13. 20 Karlsson F, Tremaroli V, Nielsen J, et al . Assessing the human gut microbiota in metabolic diseases. Diabetes 2013;62:3341 9. 21 Backhed F, Ding H, Wang T, et al . The gut microbiota as an environmental factor", + "interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev 2011; 12: 27281. 47 Kootte RS, Vrieze A, Holleman F, et al. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab 2012; 14: 11220. 48 Qin J, Li Y , Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490: 5560. 49 Karlsson FH, Tremaroli V, Nookaew I, et al. Gut metagenome in", + "Other factors Interest in the role of the gut microbiome in the devel - opment of T2DM has exploded in the past few years, and variation in the diversity and composition of the gut microbiota has been tied to T2DM100. For example, levels of butyrate-producing bacteria are decreased in the gut microbiota of patients with T2DM compared with that of healthy individuals101. In addition, evidence suggests that ambient air pollution is an emerging risk factor for", + "52. Parks, B.W., et al., Genetic control of obesity and gut microbiota composition in response to high -fat, high -sucrose diet in mice. Cell Metab, 2013. 17(1): p. 141 -52. 53. Org, E., et al., Genetic and environmental c ontrol of host -gut microbiota interactions. Genome Res, 2015. 25(10): p. 1558 -69. 54. McKnite, A.M., et al., Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLoS One, 2012. 7(6): p. e39191." + ], + "answer": "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes through various mechanisms. The interactions between host genetics, metabolism, and the immune system are crucial in shaping the microbiome and influencing the predisposition to diseases like diabetes [1]. Research suggests that the gut microbiota affects numerous biological functions throughout the body, and its characterization has become a major research area in biomedicine. Studies have shown that gut bacteria play a fundamental role in diseases such as obesity and diabetes [6]. Dysbiosis, or an imbalance in the gut microbiota, is associated with obesity and type 2 diabetes (T2D), indicating that the composition of the gut microbiome can influence metabolic diseases [6]. Moreover, genetic control of the gut microbiota composition has been observed, suggesting that host genetics can influence the microbiome, which in turn can modulate metabolic traits and potentially affect the risk of developing diabetes [10]. This implies a bidirectional relationship where genetics can shape the microbiome, and the microbiome can influence the expression of genetic predispositions to diabetes. In summary, the gut microbiome interacts with host genetics and metabolism, potentially modulating the risk of diabetes by influencing metabolic processes and immune responses [1], [6], [10].", + "question": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_5 new file mode 100644 index 0000000..ea7ed9c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "2014 - Do physicians think genomic medicine will be useful for patient care.pdf" + ], + "extraction_id": [ + "6b4157fa-dcf0-5b70-b508-38ffb5fcda8d", + "aff84b9e-3855-5960-accd-dcac6b362346", + "a500eb31-13d8-5a0f-adfc-d260189a7555", + "a0ebb8e0-1414-52f4-aa8d-9bde3a9f26c2", + "8d323598-fdf7-56cf-8290-be85929f0eaf", + "8d323598-fdf7-56cf-8290-be85929f0eaf", + "20ba070b-900d-5213-9b38-d53492e48532", + "7079e9da-e08b-5e9f-ad3d-4709915aa9e0", + "493e5840-f65b-5245-8f07-126e1d9eedc3", + "5feb39eb-3945-5a31-9d03-7b83766df1e1" + ], + "document_id": [ + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "5418b59c-465c-5b1e-aee1-52ca7a1ead52" + ], + "id": [ + "chatcmpl-AIHJ0Y0u83bg8S6UfGkVEYfllY6XI", + "a1e1110f-551a-5481-bbd6-0495d8effd38", + "73df0579-3c65-590b-9293-e0dc8fa32863", + "d0503557-1135-531d-9316-d3be3f620f3f", + "4a73b79f-f3bb-5341-865d-c6162c2f4b98", + "9fc6ebb2-02a9-5bc3-9623-6cd353ada65e", + "c15a7933-675c-5790-9165-9fef8c091920", + "d3d65022-c072-5880-8d27-a95b285e77cd", + "d8b7be7f-3f83-5f1d-897b-da01d2a7baaf", + "83b5d15e-5c0e-5abd-aa88-1affe9148052", + "065cb845-8ff9-5919-8e1f-7d2604a52e34" + ], + "contexts": [ + "All the mentioned models rely on tabular datasets such as PIMA and ECG signals [ 47] in classifying the records with possible diabetic illnesses. The current study considers that genomic data yields a better patient-centric outcome than tabular data. 2.3. Genomics for Type 2 Diabetes Many research studies have been carried out on genetic-based illness prediction. Incorporating machine learning approaches with genetic-based illness prediction could", + "- chondrially rich, provides a direct connection between physiological dysfunction observed in the heart and the impact of altered genomic profiles in the mitochondrion and nucleus. Machine-learning, which at current has been applied to very few genetic applications, may play a significant role in defining the epigenome of those with diabetes mellitus, likely unveiling genes and molecular pathways first impacted by the pathology. The challenges ofmachine learning intheclinical setting", + "15. Ali, M.M.; Paul, B.K.; Ahmed, K.; Bui, F.M.; Quinn, J.M.W.; Moni, M.A. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput. Biol. Med. 2021 ,136, 104672. [CrossRef] 16. Bell, C.G.; Teschendorff, A.E.; Rakyan, V .K.; Maxwell, A.P .; Beck, S.; Savage, D.A. Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus. BMC Med. Genom. 2010 ,3, 33. [CrossRef]", + "Diagnostics 2022 ,12, 3067 6 of 30 Table 1. Various existing models for diabetes prediction. Approach Type of Data Applicability Limitations polygenic scores-based approach [12]Genomic DataUsed in the evaluation of clinical trials and illness screening mechanismsThe polygenic score approach needs larger samples and tremendous training for considerable Accuracy. Singular Value Decomposition [13]Genomic Data Tabular Data The image they are usedThey are used in ranking the feature", + "In the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, bio - chemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status. In 50 patients, machine-learning algorithms revealed the interconnectedness between dia - betic classification, mitochondrial function, and methyla -", + "Diabetes mellitus is a multifaceted disease, consisting of systemic comorbidities which necessitate a variety of treatment modalities and stratify those affected with the disease [5]. Before the implementation of machine-learning algorithms in medicine, linear statistical models have highlighted measures, such as HbA1c, as diagnos - tic staples for the evaluation of diabetes mellitus onset and progression [6]. By exploring these previously pub -", + "tool that combines both genetic and clinical featur es in order to identify diabetic nephropathy in patients with T2D [81]. Leung et al . compared several machine learning methods that include partial least square regression, classification and regression tree, the C5.0 Decision Tree, Random For est, naive Bayes, neural networks and support vector machines [82]. The dataset used consists of both genetic (Single Nucleotide Polymorphisms - SNPs) and clinical data. Age, age of diagnosis, systolic", + "- ylation status and total nuclear methylation provided the best predictive measures for assessing type 2 diabetes mellitus. The incorporation of physiological, biochemical, genetic, and epigenetic features with machine-learning algorithms exemplifies the potential for more informa - tive diagnostics in the future, as well as personalized approaches to generalized treatment modalities (Fig.6). Discussion Machine-learning can be applied as a systems biol -", + "- tures is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient. Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled popu - lation grows. Conclusions Our work highlights the importance of identifying bio -", + "10 Meigs JB, Shrader P, Sullivan LM et al. Genotype score in addition to common risk factors for prediction of Type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 11 Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA 299, 13201334 (2008). \t Systematic\treview\tof\tearly\tresearch\tinto\tgenomic\tmedicine \t adoption\tin\tthe\tclinical\tcare\tof\tcommon\tchronic\tdiseases. \t Outlines\tboth\tphysician\tand\tpatient\tperspectives\ttowards" + ], + "answer": "Machine learning algorithms show promise in integrating genomic data to predict individual risk and progression of diabetes, but there are challenges and limitations to consider. 1. Genomic data is considered to yield better patient-centric outcomes than traditional tabular data for predicting diabetic illnesses [1]. This suggests that machine learning models that incorporate genomic data may provide more accurate predictions. 2. Machine learning has been applied to integrate various types of data, including genomic and epigenomic biomarkers, to determine type 2 diabetic status. This approach has revealed connections between diabetic classification and other biological functions, indicating the potential of machine learning in this area [5]. 3. The integration of physiological, biochemical, genetic, and epigenetic features with machine learning algorithms has shown potential for more informative diagnostics and personalized treatment approaches for diabetes [8]. 4. However, there are limitations, such as the need for larger sample sizes and extensive training to achieve considerable accuracy when using polygenic scores-based approaches with genomic data [4]. Overall, while machine learning algorithms have demonstrated potential in integrating genomic data for diabetes prediction, further research and development are needed to overcome current limitations and improve accuracy and applicability in clinical settings.", + "question": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_6 new file mode 100644 index 0000000..79e927c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2019 - Development and Genome Sequencing.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "2016 - A genetic screen identifies hypothalamic Fgf15 as a regulator of glucagon secretion.pdf" + ], + "extraction_id": [ + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "8bbfb009-87b7-54ae-8465-8796db8c271a", + "d7b2d258-d566-5552-8308-4ac35953884d", + "d971dced-935c-566b-a4a2-11bcf99b9c84", + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "efc73cf6-99c6-5272-9bb0-7bd6a34633f0", + "a847f5f4-0c56-5678-9e1e-93b9b5b294f2" + ], + "document_id": [ + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "18820c9e-f7ae-57ae-897d-0d9c3f616b6a", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "019efefb-65db-55f5-a3a7-4f224473f51f", + "288adb9b-a547-5e61-8593-1b2ab36271d3" + ], + "id": [ + "chatcmpl-AIHJ7kKFoNZYhA6ZvKYEgyC7wipHg", + "66b05301-179b-597c-bb68-e6fd0e0d1d5a", + "e85449e5-801e-5431-80e1-521699d18780", + "2d9e043b-a3fa-52dc-9a4e-71ed49f9ec1d", + "a0146183-d255-5eae-85eb-adaf007d1b32", + "b3c5f734-aa0d-5da9-bdb9-e330e6c02e00", + "b774bf7b-4546-56d2-ae7b-7bc2c9f2fb08", + "c8d55dea-0656-527e-93bd-9624cec8f3c9", + "e5669569-f9ba-5797-b468-3a1980addc0a", + "9ca17d26-cc06-5afe-a7dd-3f80b1b99da0", + "45d35985-9183-55f0-8b51-41df27cd7677" + ], + "contexts": [ + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "this would require further investiga-tions, both invivo and invitro and critical networking among researchers, clinicians, and patients. Nevertheless, the implications of lncRNAs in diverse facets of insulin resistance and diabetes are indicative of their roles in the diagnosis, prognosis, and therapy of this disease in future.", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "58. You L, Wang N, Yin D etal (2016) Downregulation of long noncoding RNA Meg3 affects insulin synthesis and secretion in mouse pancreatic beta cells. J Cell Physiol 231:852862 59. Arnes L, Akerman I, Balderes DA, Ferrer J, Sussel L (2016) betalinc1 encodes a long noncoding RNA that regulates islet beta-cell formation and function. Genes Dev 30:502507 60. Akerman I, Tu Z, Beucher A etal (2017) Human pancreatic beta cell lncRNAs control cell-specific regulatory networks. Cell Metab 25:400411", + "of lncRNAs in the development and function of metabolic tissues, and therefore, their altered levels are closely asso-ciated with the onset and progression of insulin resistance and diabetes. Roles oflncRNAs indiabetic complications Apart from being involved in major metabolic tissues dur -", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and func-tion [ 54]. To understand the roles of lncRNAs in regu- lating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancre-atic islets [55, 56]. Transcriptome analysis in pancreatic -cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR -", + "1831 Lnc-ing non- coding RNAs withmetabolism anddiabetes: roles oflncRNAs 1 3 endocrine hormones, insulin and glucagon, where insulin is the anabolic master regulator which controls periph -", + "Vol.:(0123456789)1 3Cellular and Molecular Life Sciences (2018) 75:18271837 https://doi.org/10.1007/s00018-018-2760-9 REVIEW Lncing noncoding RNAs withmetabolism anddiabetes: roles oflncRNAs NehaGoyal1,2 DeveshKesharwani1,2 MalabikaDatta1,2 Received: 18 September 2017 / Revised: 29 December 2017 / Accepted: 24 January 2018 / Published online: 31 January 2018 Springer International Publishing AG, part of Springer Nature 2018 Abstract", + "(2013). A novel mechanism regulating insulin secretion involving Herpud1 inmice. Diabetologia 56, 15691576 . Zhao, X.Y., and Lin, J.D. (2015). Long noncoding RNAs: a new regulatory code in metabolic control. Trends Biochem. Sci. 40, 586596 . 1806 Cell Reports 17, 17951806, November 8, 2016" + ], + "answer": "Recent findings highlight the significant role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity, with several implications for understanding and potentially treating diabetes. 1. **Regulation of Islet Function**: LncRNAs have been shown to regulate the development and function of pancreatic islets, which are crucial for insulin secretion. For instance, the lncRNA H19 is involved in this regulatory process [1]. This suggests that lncRNAs could be critical in maintaining normal insulin secretion and could be targets for therapeutic intervention in diabetes. 2. **Impact on Insulin Synthesis and Secretion**: Specific lncRNAs, such as Meg3, have been found to affect insulin synthesis and secretion in pancreatic beta cells [4]. This indicates that lncRNAs play a direct role in the cellular mechanisms that control insulin production, which is essential for maintaining glucose homeostasis. 3. **Therapeutic Potential**: Due to their specific functions in regulating cellular pathways, lncRNAs are considered promising therapeutic targets. Their expression patterns in tissues often correlate with the progression of diabetes, making them potential biomarkers for diagnosis and prognosis [3]. 4. **Association with Insulin Resistance and Diabetes**: Altered levels of lncRNAs are closely associated with the onset and progression of insulin resistance and diabetes [5]. This association underscores the potential of lncRNAs as targets for interventions aimed at improving insulin sensitivity and managing diabetes. 5. **Research and Clinical Implications**: The diverse roles of lncRNAs in insulin resistance and diabetes suggest their importance in future research for diagnosis, prognosis, and therapy of the disease [2]. This calls for further investigations and collaborations among researchers, clinicians, and patients to fully understand and harness the potential of lncRNAs in diabetes management. Overall, these findings imply that lncRNAs are integral to the regulation of insulin secretion and sensitivity, offering new avenues for research and treatment strategies in diabetes.", + "question": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_7 new file mode 100644 index 0000000..7a9b884 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - MicroRNA profiling and their pathways in South African.pdf", + "2015 - Type 2 diabetes mellitus.pdf", + "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "2004 - Diabetes Genes a.pdf", + "2011 - CREB and ChREBP oppositely regulate SIRT1 expression in response to energy availability.pdf", + "2015 - Transcript Expression Data from Human.pdf", + "2018 - A computational biology approach of a genome-wide screen.pdf", + "2015 - Type 2 diabetes mellitus.pdf", + "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf" + ], + "extraction_id": [ + "14577d73-d320-54dd-93f2-c55f986bc8bc", + "617b6fab-a5e5-59b7-a593-a0477e6bf9fe", + "bf537fe8-5508-5355-a656-b4053febe0e5", + "06912a59-fdd6-5731-af8f-6c98ff1ace5c", + "4217906f-87c5-54b0-95a5-7c26dc08afce", + "867d0b1b-16a1-53ea-b014-3c204b9001a5", + "ab4f6ea7-767f-5783-9e1a-8570eaabe96c", + "e4e89eba-6032-5781-83f4-8d47ab5b3825", + "283e34bb-6e2b-5aa9-85c5-2584b669f122", + "41ac576d-b850-5ee8-9753-ba9b060ba798" + ], + "document_id": [ + "b6bb090d-7176-59db-af04-582aa1d5cf10", + "415516ba-5365-501b-84ce-0789045862f8", + "be05127e-1be8-5573-b571-51a11c3b2be2", + "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "32538f01-9eaf-5f9b-8615-ec47cc4ca8e2", + "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "ae727c80-a0a3-52f4-9e2f-b93a539558ad", + "415516ba-5365-501b-84ce-0789045862f8", + "815d7f3e-e219-502f-aba0-57a68ae787d3", + "766edfd5-4756-51bf-b636-c94b041d030c" + ], + "id": [ + "chatcmpl-AIHJKDULZO7wnGlvKQDCFnpw8Cumo", + "64482aec-5688-5431-adda-b8f1de92a183", + "b604dabf-3dc2-5d01-9cc4-6e9f916c464a", + "e1f984ac-aa42-5eb4-92cb-303886f6f1db", + "7b6e89ec-b690-5ff1-b24d-3ed6744f3486", + "9a8edd2d-c06a-559e-8397-beaaa84705b7", + "7d522337-e875-55eb-9b67-4718e5db8ffd", + "1edee360-5de0-51c9-bf8d-7c2e2f23a682", + "43a104b3-f34b-5f52-86ff-fd7d45827f32", + "3e08ef82-888b-58a0-9a80-3547ab4bd516", + "cf4f3239-dd62-5eef-b5fc-85f4780e3f48" + ], + "contexts": [ + "regulates glucose-induced biological responses in pancreatic beta-cells. Diabetes. 2008;57:2708-17. 29. Schultze SM, Hemmings BA, Niessen M, Tschopp O. PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis. Expert Rev Mol Med. 2012;14:e1. 30. White MF. IRS proteins and the common path to diabetes. Am J Physiol Endocrinol Metab. 2002;283:E413-22. 31. Erener S, Marwaha A, Tan R, Panagiotopoulos C, Kieffer TJ. Profiling of circulating microRNAs in children with", + "pathological processes involved in glucose metabolism by post transcriptional regulation of gene expression. Particular microRNAs can regulate cell function271, exposing key regulatory signalling pathways involved in restoration of cell mass, and provide a promising strat egy for improving insulin secretion and cell health in T2DM. Identification of novel insulin secretagogues that act directly on cells and enteroendocrine Kcells and Lcells in the intestine are under investigation, and", + "can result in diabetes and its complications including DN. Several studies show that key histone post- translational modifications are involved in the regulation of genes associated with the pathogenesis of diabetes, such as insulin and islet-specific transcription factors.48,60 Inaddi - tion, several groups are examining the role of histone post-translational modifications in adipocytes related to type2 diabetes, obesity and the metabolic syndrome.48,60", + "cascade of protein kinases and regulatory proteins of which IRS-1 and IRS-2 are most important. This causes suppression of glucose release from liver and kidney/ translocation of glucose transporters in muscle and adipose tissue to increase their glucose uptake, and inhibition of release of FF A into the circulation due to suppression of the activity of hormone-sensitive lipase and a simultaneous increase in their clearance from the circulation. Although", + "Magnan C, Postic C, Prip-Buus C, Vasseur-Cognet M (2008) The transcription factor COUP-TFII is negatively regulated by insulin and glucose via Foxo1- and ChREBP-controlled pathways. Mol Cell Biol 28: 65686579Rodgers JT, Lerin C, Haas W, Gygi SP, Spiegelman BM, Puigserver P (2005) Nutrient control of glucose homeostasis through a complex ofPGC-1alpha and SIRT1. Nature 434: 113118 Schwer B, Verdin E (2008) Conserved metabolic regulatory functions of sirtuins. Cell Metab 7:104112", + "of glucose transporter 2 glycosylation promotes insulin secretion in suppressing diabetes. Cell 123:1307 1321. PMID: 16377570 47. Whitaker GM, Lynn FC, McIntosh CH, Accili EA (2012) Regulation of GIP and GLP1 receptor cell sur- face expression by N-glycosylation and receptor heteromerization. PLoS One 7: e32675. doi: 10.1371/ journal.pone.0032675 PMID: 22412906 48. Johswich A, Longuet C, Pawling J, Abdel Rahman A, Ryczko M, et al. (2014) N-glycan remodeling on", + "strate 1), Pde3b (phosphodiesterase 3B), Hk2 (hexokinase 2), Foxo1 (forkhead box O1), Socs6 (suppressor of cytokine signaling 6), and Ogt (O-linked N-acetylglucosamine (GlcNAc) transferase). Impaired insulinsignaling is well known to negatively in uence glucose and lipid metabolism [62]. In adipose tissue, insulin stimulates glucose uptake by inducing translocation of GLUT4 to the cell surface, it increasesglycolysis rate by stimulating hexokinases ( Hk2) and suppresses lipolysis ( Acaca and Prkaa1 )[63].", + "signalling pathways by reducing insulin induced tyro sine phosphorylation of IRS1 and IRS2 (REF. 161) and by increasing degradation of IRS1 (REF. 162). Recent studies have demonstrated that the p85 regulatory subunit of PI3K interacts with XBP1s (the spliced, transcription ally active isoform of XBP1) and promotes the trans location of XBP1s into the nucleus to initiate the ER stress response163.Diabetic complications Diabetic microvascular complications are closely related", + "activated protein kinase. J Biol Chem. 2007;282:9777 -88. [44] Chakrabarti S, Davidge ST. High glucose -induced oxidative stress alters estrogen effects on ERalpha and ERbeta in human endothelial cells: reversal by AMPK activator. J Steroid Biochem Mol Biol. 2009;117:99 -106. [45] Mortuza R, Chen S, Feng B, Sen S, Chakrabarti S. High glucose induced alteration of SIRTs in endothelial cells causes ra pid aging in a p300 and FOXO regulated pathway. PLoS One. 2013;8:e54514.", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362" + ], + "answer": "Post-translational modifications (PTMs) of proteins play a significant role in regulating key signaling pathways involved in glucose homeostasis. Here are some ways PTMs affect these pathways: 1. **Protein Kinases and Glucose Homeostasis**: The PI3K/AKT, MAPK, and AMPK signaling pathways are crucial for glucose homeostasis, and these pathways are regulated by protein kinases, which can be modulated by PTMs such as phosphorylation [1]. 2. **Histone Modifications**: Histone post-translational modifications are involved in the regulation of genes associated with diabetes pathogenesis, including those related to insulin and islet-specific transcription factors. These modifications can influence gene expression and thereby affect glucose metabolism [3]. 3. **N-glycosylation**: The glycosylation of glucose transporter 2 is an example of a PTM that promotes insulin secretion, which is vital for maintaining glucose levels and suppressing diabetes [6]. 4. **Sirtuins and Deacetylation**: The sirtuin family, particularly SIRT1, is involved in regulating factors related to metabolism and insulin secretion. Sirtuins are deacetylases, and their activity represents a form of PTM that can influence glucose homeostasis [10]. These examples illustrate how PTMs can modulate signaling pathways and protein functions, ultimately impacting glucose homeostasis and related metabolic processes.", + "question": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_8 new file mode 100644 index 0000000..cb827b8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "2008 - Learning From Molecular Genetics.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "2010 - Family History of Diabetes and Prevalence.pdf", + "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf" + ], + "extraction_id": [ + "0f16b510-caa9-521f-8d87-e225f52de9f5", + "744f3821-fc61-58d1-8107-17d5674fe1d8", + "979b0578-b02a-526e-b3b4-aa7fec3eeb91", + "f01be500-1e96-57ca-b164-1b97017ec44d", + "782db3c2-ab52-59df-b798-c5b2492c8946", + "744f3821-fc61-58d1-8107-17d5674fe1d8", + "5a39ee4b-ba00-56d6-ba6c-0edeac3b4f2e", + "31b3b484-d769-5b88-9633-45ca3508be8f", + "59b6bf24-b9f7-53bc-b809-5ce97d5e9898", + "36a362bd-1d4a-5e47-a00f-5ae6d3a0626f" + ], + "document_id": [ + "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "1fa2280b-868e-571f-830e-bebdd874fc9d", + "3aa0aea1-fe53-519b-b367-79fa1c79aa01", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "1fa2280b-868e-571f-830e-bebdd874fc9d", + "1fa2280b-868e-571f-830e-bebdd874fc9d", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "f16c4c6e-bb5f-5d4a-9945-8af4d0df19f4", + "18a8a000-69ed-5d34-b13f-f5ae016d1067" + ], + "id": [ + "chatcmpl-AIHJRHE43F2P7lugdT3xuuPfMvKGY", + "d0465c0a-e45a-5fff-a9b3-40ad191963a3", + "f4b3a7bd-4a5c-5c95-aa11-936340eecb8b", + "e4240b4f-b19f-5f8c-b9ce-e0b60cf4aceb", + "3efa9423-0651-5096-8ccd-50a0f906e30e", + "1ae4ea48-5dbd-5502-aa74-a1ef1527a493", + "e0234ab8-850f-585c-9de2-edaa3ac615fd", + "85de5eb6-8034-5ebb-8526-1de0a173b2ae", + "0b2437f9-1aff-5e6a-bca6-cc17d96c5d56", + "8fbdd64f-d761-5e8d-989c-7e4bf376b1ec", + "bd8d61dd-e027-5a43-8b00-1e7e9e748ea3" + ], + "contexts": [ + "WFS1 and genotype-phenotype correlation in Wolfram syndrome. Am J Med Genet A. 2007;143A(14):1605 12. 61. McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60(5):793 9. 62. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41 7. 63. Patch AM, Flanagan SE, Boustred C, Hattersley AT, Ellard S. Mutations in the ABCC8 gene encoding the SUR1 subunit of the KATP channel cause", + "enable physicians to ameliorate some of the complications that so devastate the lives of these patients. Three questions need answers from further studies: is there really a lack of diabetic complications in Wolfram syndrome patients compared with other diabetics? What is the nature of the neurodegeneration and its relation to diabetes mellitus? Are heterozygotes for Wolfram syndrome at risk of maturity-onset diabetes? This paper is dedicated to the memory of Robin Smith, a Wolfram", + "Monogenic and syndromic forms account for only a small,though highly informative, proportion of cases of nonau-toimmune diabetes. The challenge for medical science liesin bringing equivalent mechanistic insights and transla-tional benets to the hundreds of millions of peoplealready affected by, or at risk of, more common, typicalforms of diabetes. For type 2 diabetes, there is abundantevidence that individual susceptibility is inuenced byboth the combination of genetic variation at multiple sitesand a", + "responding to two causative genes have been identified to date. Wolfram syndrome 1 (WS1), characterized by diabetes insipidus, DM, optic atrophy, and deafness, is a rare autosomal recessive disease caused by variants in wolframin ER transmembrane gly- coprotein (WFS1). Severe cases with dominant heterozygous vari- ants are also reported (92). Often, patients first manifestation is DM at an average age of 6 years. Though most WS1 patients", + "finding study to describe the natural history, complications, prevalence, and inheritance of the syndrome. We identified 45 patients with Wolfram syndrome—a prevalence of one per 770000. Non-autoimmune, insulin- deficient diabetes mellitus presented at a median age of 6 years, followed by optic atrophy (11 years). Cranial diabetes insipidus occurred in 33 patients (73%) with sensorineural deafness (28, 62%) in the second decade; renal-tract abnormalities (26, 58%) presented in the third", + "Wolfram patients have a mitochondrial genome abnormality, but this has not yet been shown. The differential diagnosis indicates the importance of accurate clinical descriptions when presenting cases of the syndrome. Our study has implications for basic science and practice: more accurate characterisation of the syndrome will allow assessment of genotype/phenotype correlations; and earlier recognition of diabetes insipidus, gastrointestinal dysfunction, and central apnoeas should", + "onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.Monogenic diabetes: a gateway to precision medicine in diabetes Haichen Zhang,1 Kevin Colclough,2 Anna L. Gloyn,3,4 and Toni I. Pollin1", + "WFS1 mutations underlie a genetic syndrome of neonatal/infancy-onset diabetes, congenital sensorineural deafness, and congenital cataracts. Diabetes . 2017;66(7):20442053. 93. Rigoli L, Di Bella C. Wolfram syndrome 1 and Wolfram syndrome 2. Curr Opin Pediatr. 2012;24(4):512517 . 94. Bansal V, et al. Identification of a missense vari- ant in the WFS1 gene that causes a mild form of Wolfram syndrome and is associated with risk for type 2 diabetes in Ashkenazi Jewish individuals.", + "established. It has been corroborated by a series of obser-vations that include ethnic differences, familial aggrega-tion, twin studies, admixture studies, linkage studies, monogenic cases (e.g., MODY), mitochondrial cases of diabetes, and a constantly growing number of molecular markers [5] . On the other hand, the genetics of the meta- bolic syndrome remains complex [6] . It is highly unlikely that a single gene will account for a substantial portion", + "diabetes (0.5% carrier frequency) compared to controls (0.035%). One individual with early onset diabetes was homozygous for a rare pathogenic missense variant in the WFS1 gene but did not have the additional phenotypes associated with Wolfram syndrome. Conclusion: Targeted sequencing of genes linked with monogenic diabetes can identify disease-relevant mutations in individuals diagnosed with type 2 diabetes not suspected of having monogenic forms of the disease. Our data suggests" + ], + "answer": "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome, has provided several insights: 1. **Genotype-Phenotype Correlation**: Research has highlighted the importance of understanding genotype-phenotype correlations in Wolfram Syndrome. This involves identifying specific genetic mutations and understanding how they manifest in clinical symptoms, which can aid in more accurate diagnosis and management of the syndrome [1], [6]. 2. **Genetic Mutations**: Wolfram Syndrome is primarily caused by mutations in the WFS1 gene, which encodes the wolframin ER transmembrane glycoprotein. These mutations lead to a range of symptoms including diabetes mellitus, optic atrophy, and deafness. Some severe cases are associated with dominant heterozygous variants [4]. 3. **Early Onset and Symptoms**: The syndrome is characterized by early onset of diabetes mellitus, typically around the age of 6, followed by other symptoms such as optic atrophy and sensorineural deafness in later years [5]. 4. **Potential for Broader Implications**: Insights from studying Wolfram Syndrome have implications for basic science and clinical practice. They emphasize the need for accurate clinical descriptions and early recognition of symptoms, which can improve patient outcomes and inform treatment strategies [6]. 5. **Risk Assessment**: There is ongoing research to determine if heterozygotes for Wolfram Syndrome are at risk for maturity-onset diabetes, which could have implications for understanding genetic risk factors in broader populations [2]. These insights not only enhance our understanding of Wolfram Syndrome but also contribute to the broader field of genetic research in diabetes, potentially informing personalized medicine approaches for more common forms of the disease.", + "question": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_9 new file mode 100644 index 0000000..3e61804 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_diabetes_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Interaction Between Exercise and Genetics.pdf", + "2010 - A Genome-Wide Association Study of Treated A1C.pdf", + "2004 - Common polymorphisms of the PPAR-\u03b32 (Pro12Ala) and PGC-1\u03b1 (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial.pdf", + "2016 - Hypomethylation within gene promoter regions and type 1 diabetes.pdf", + "2010 - Genome-scale approaches to the epigenetics of common.pdf", + "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "2003 -Genetic epidemiology of type 1 diabetes.pdf", + "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "2013 - Continuous Aging of the Human DNA Methylome.pdf", + "2001 - The genetics of type 2 diabetes.pdf" + ], + "extraction_id": [ + "861346c7-0fcf-5cae-ace6-a012a370d297", + "cce780d7-60c0-5cb3-976f-15e9808cab59", + "feb52f56-db94-5e03-90a8-af3bf38d087e", + "bc569d05-fc39-5487-95e7-63b0d7bf9b7e", + "8881623e-fe7a-53bd-b703-2e8bf6a5c240", + "2778ece8-df84-58d2-9002-e036f0d007dc", + "592fd011-4dfb-5a78-8973-482e35541468", + "551087b1-8e80-5a7b-839a-304f566a6417", + "a0916b04-3463-5247-94da-0c97fd5da20d", + "737e4fe2-91ba-50c5-8f64-1149944fb60c" + ], + "document_id": [ + "c36db75e-4b76-540d-9efb-d0e156e61541", + "4de9f054-4a02-5b6a-905d-420744075755", + "07b6ab4b-1aff-5d0e-aa98-d49a66b7b5d1", + "3d0f1bab-738c-5ba3-8fd9-206b3455704d", + "3f21702c-3fdf-50b5-8060-04a9a1ce09ae", + "17637a6f-804e-50e4-9cf5-37318e17f15c", + "cbc7f2d3-3f65-50ba-b281-96dd1c77f2c0", + "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "34783678-d4c2-566f-9b5b-0db18a72e86d", + "8ab06972-1c6c-5d68-a270-65fb0af0917b" + ], + "id": [ + "chatcmpl-AIHJckmqoH46qp1BDk6zYdLg1Akw5", + "6623716f-8275-5f17-be27-a8241560a645", + "15e73a17-cae0-5dc1-8cad-5805809ae872", + "c57caee6-9b8f-5cb5-ab53-337dda1d92a7", + "bcfe4c03-f0bb-540d-b529-adda644998a6", + "4e393756-1b48-56f7-a3e7-33d15a55dc73", + "a4c3abc2-635c-5ace-ad39-c678a505fa48", + "f295f1d3-43d5-5eef-a39f-a8b91c47500f", + "0cd29c12-48e8-5f9f-9744-6b8acfaae0c7", + "3a9e7574-8914-5a96-86b6-b7b87a89b894", + "af680560-47c6-5556-bb80-c7584d762f66" + ], + "contexts": [ + "Studies of twins also provide compelling evidence for a genetic component to T2D. Estimates for concordance rates range from 0.29 to 1.00 in monozygotic (MZ) twins, while in dizygotic (DZ) twins the range is 0.100.43 [57, 58, 6164]. The high levels of heritability observed for insulin sensitivity and insulin secretion [6567] further reinforce the role of genetics in diabetes and indicate the primary genetic lesions for diabetes are likely to localize to genes in beta-cell-centric pathways.", + "It is therefore intriguing that A1C levels are signicantly correlated in monozygotic twins whether they are concor- dant for type 1 diabetes or not (4): in a discordant twin pairone twin is treated with insulin, whereas the other oneisnt, and thus this degree of correlation suggests thatgenetic contributors to A1C may be detectable despite thesuperimposition of a strong environmental modier. Rig-orous estimates of heritability of treated A1C, however, are not available.", + "Concordance rate for type II diabetes mellitus in monozy-gotic twins: actuarial analysis. Diabetologia 42:146150 3. Lehtovirta M, Kaprio J, Forsblom C, Eriksson J, Tuomilehto J, Groop L (2000) Insulin sensitivity and insulin secretionin monozygotic and dizygotic twins. Diabetologia43:285293 4. Florez JC, Hirschhorn J, Altshuler D (2003) The inherited basis of diabetes mellitus: implications for the genetic anal-ysis of complex traits. Annu Rev Genomics Hum Genet4:257291", + "disease susceptibility is not explained by genetics alone; environ- mental factors, gene by environment interactions, and epigenetic inuences are likely to play important roles in the etiology of T1D [5,6] . Monozygotic (MZ) twin pairs, discordant for T1D, represent an ideal system to test susceptibility factors not attributable to genetic variation, especially epigenetic variation, since the ge- nomes of the twins are identical. The ascertainment of disease-", + "epigenetic differences among monozygotic twins. A critical question is whether epigenetic marks are transmitted intactfrom parent to offspring and whether DNAm is allele- specific and covaries with allele-specific gene expression. For example, can we develop an epigenetic transmissiontest comparable to the transmission disequilibrium test used in genetic epidemiology? Finally, and most excitingly, we", + "their dietary and physical activity habits (Maes et al, 1997 ). There is also ample evidence that diabetes has a substantial genetic component. The con- cordance of type 2 diabetes in monozygotictwins ranges between 50 and 70% compared to 2037% in dizygotic twins (Kaprio et al, 1992 ; Newman et al, 1987 ; Poulsen et al 1999). Further evidence comes from studies that compare therisk in offspring with a family history of type 2 diabetes with offspring without such a fam-", + "monozygotic and dizygotic Danish twin pairs withinsulin dependent diabetes mellitus. Bmj 1997: 314:1575 1579. 30. R EDONDO MJ, R EWERS M, Y UL et al. Genetic deter- mination of islet cell autoimmunity in monozygotictwin, dizygotic twin, and non-twin siblings of patientswith type 1 diabetes: prospective twin study. Bmj 1999:318: 698 702. 31. L EVY-M ARCHAL C, P ATTERSON C, G REEN A. Variation", + "Studies in twins have demonstrated that 5070 % in the body mass index (BMI) variance may be explained by genetics ( Allison et al., 1996 ), and T2DM concordance was reported ranging from 1737 % in dizygotic to 5070 % in monozygotic twins ( Kaprio et al., 1992 ; Medici et al., 1999 ; Poulsen et al., 1999 ). In addition, family and adoption studies have reported heritability ranging from 2060 % for obesity ( Rice et al., 1999 ; Stunkard et al., 1986 ) and 3070 % for T2DM ( Meigs", + "Monozygotic twins exhibit numerous epigenetic differences: clues to twindiscordance? Schizophr Bull 29: 169178. 8. Oates NA, van Vliet J, Duffy DL, Kroes HY, Martin NG, et al. (2006) Increased DNA methylation at the AXIN1 gene in a monozygotic twin from a pair discordant for a caudal duplication anomaly. Am J Hum Genet 79: 155162. 9. Kuratomi G, Iwamoto K, Bundo M, Kusumi I, Kato N, et al. (2008) Aberrant DNA methylation associated with bipolar disorder identified from discordant", + "5 E/C128orts to estimate the heritability of T2D by a comparison of the concordance rates in mono- and dizygotic twins have varied greatly as a result of di/C128erences in ascertainment scheme, diagnostic criteria and follow-up duration.69Concordance for diabetes is generally higher in identical twins (supporting a genetic basis for disease), although the extremely high concordance rates in some early studies6were undoubtedly inated by ascertainment bias. Evidence from population studies" + ], + "answer": "The study of monozygotic twins discordant for diabetes provides valuable insights into the etiology of the disease by highlighting the roles of both genetic and epigenetic factors. Since monozygotic twins share identical genomes, any differences in disease manifestation between them can often be attributed to non-genetic factors, such as epigenetic modifications or environmental influences. 1. **Genetic Component**: The high concordance rates for diabetes in monozygotic twins, compared to dizygotic twins, underscore a significant genetic component to the disease [1], [6]. This suggests that genetic predispositions, particularly those affecting insulin sensitivity and secretion, play a crucial role in the development of diabetes [1]. 2. **Epigenetic Influences**: Despite identical genetic makeup, monozygotic twins can exhibit differences in disease susceptibility due to epigenetic variations. These variations can arise from environmental factors and gene-environment interactions, which are crucial in understanding the etiology of diabetes [4]. Epigenetic differences, such as DNA methylation patterns, have been observed in monozygotic twins and may contribute to discordance in disease states [9]. 3. **Environmental and Lifestyle Factors**: The discordance in diabetes among monozygotic twins also points to the influence of environmental factors and lifestyle choices, such as diet and physical activity, which can modify epigenetic marks and affect disease outcomes [6]. In summary, the study of monozygotic twins discordant for diabetes highlights that while genetic predispositions are significant, epigenetic modifications and environmental factors also play critical roles in the disease's etiology. This understanding can help in developing more targeted prevention and treatment strategies that consider both genetic and non-genetic factors.", + "question": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_1 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_1 new file mode 100644 index 0000000..5260530 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_1 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2005 -Pomp- GenomeExploitation.pdf", + "2006 - Marker Assisted Backcrossing .pdf", + "2013 - Host Genes and Resistance.pdf", + "2014 - Fine-mapping QTLs in advanced intercross lines and other.pdf", + "2007 - Latexin is a newly discovered regulator of hematopoietic stem cells.pdf", + "2020 - Large?scale pathway specific polygenic risk and transcriptomic.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf" + ], + "extraction_id": [ + "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "80f97b13-9dd9-5d52-9d55-0abac724605e", + "da78b007-359c-548c-8cb0-ba4a3dab0f86", + "661e7fb0-804c-53e2-b948-6512c372ac57", + "a5c455c9-50f6-5f12-84cd-26d335001e6b", + "91470df2-7451-59d2-af9f-98cdf2f85486", + "a53c7001-432d-5289-9cc1-b3d75a721da4", + "4a7ed73c-26c7-5852-8a02-a39cd0d611ec", + "cec82840-0f05-5fc5-bfcf-a4b928124fef" + ], + "document_id": [ + "c54da858-9620-588e-8e41-76a960af2ff6", + "17264155-b665-59db-94cb-f4d67eac20fc", + "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "5efc1bdf-f847-5eaf-a808-9cf71b9399ce", + "cc6cf2b5-0440-51e8-aad4-d0b4b5331ab2", + "eb30392e-f079-511d-8c6c-a6e6c98d2167", + "63467ba8-940b-59f6-bbd5-0b0ce7883d49", + "96119357-a6dd-5ea3-8bcb-9c047f0a336e", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "263d327b-f5db-54e4-a215-b3f8a51cd7d6" + ], + "id": [ + "chatcmpl-AIGrF12QPoEwoc2D22aSA5ivwYW2D", + "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "e26ebc1e-e05d-56fb-8718-604275994a84", + "b17b43c6-1ba8-5849-8664-3b5cd78877b5", + "037c669c-da80-5e1e-abe3-c4344145a4ed", + "fb5944f3-bb0e-599e-827c-a8b7c6934746", + "a860695e-fe40-55eb-9eb8-072e1daf5cf2", + "22301737-122c-57be-a2f1-9d631ad101b3", + "101c1f27-4a98-5d1c-b013-c5f1950aee95", + "91ac7cb9-ec59-5bd6-9f24-aa840caf2c27", + "6e933f07-26d6-5cf1-8ee0-9bf6ec68b1ff" + ], + "contexts": [ + "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", + "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "42.Chesler EJ, et al. 2005. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system func-tion. Nat. Genet. 37:233242. 43.Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, develop- ing a resource for mammalian systems genetics: a status report of theWellcome Trust cohort. Mamm. Genome 19:379 381. 44.Xiao J, et al. 2010. A novel strategy for genetic dissection of complex traits:", + "multiple-SNP analysis of GWAS summary statistics identiesadditional variants inuencing complex traits. Nat Genet 44(369375):S1S3. doi: 10.1038/ng.2213 Yang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls in the application of mixed-model association methods. NatGenet 46:100106. doi: 10.1038/ng.2876 Yazbek SN, Buchner DA, Geisinger JM et al (2011) Deep congenic", + "10. The power of integrating all these genetic and genomic data has now been well documented, offering a glimpse of what the future of com-plex trait genetics will look like. Model systems that are genetically more complex, including extensive eight-strain crosses 11,12 and haplotype association studies using large panels of regular inbred strains of mice, and even humans, are", + "tive analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9:918 33. Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:7682 34. Zeisel A, Hochgerner H, Lnnerberg P, Johnsson A, Memic F, van der Zwan J etal (2018) Molecular architecture of the mouse nervous system. Cell 174:999.e221014.e22 35. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ (2016) RVTESTS:", + "used to identify molecular traits involved in the p athology of diseases and to eluci- date the networks underlying complex phenotypes. Re cent studies have pushed the genetical genomics concept further towards data int egration and interpretation within and across molecular levels, and have also r evealed remaining challenges. The focus of this review is to discuss these challe nges and their possible solutions in", + "2 large populations. The new methods have allowed us to dissect the genetic architecture of complex disorders including the identification of the causal genomic loci, estimation of the disease heritability, estimation of effect sizes of different loci and their non-additive interactions. Linkage analysis The earlier breakthroughs in linking genotype with phenotype involved studies of Mendelian disorders that can be mapped to a single gene and a single mutation. These" + ], + "answer": "Recent advancements in network-based integrative genomics have significantly enhanced our understanding of complex trait architectures by enabling a more comprehensive analysis of the interactions between genetic and environmental factors. These advancements allow for the integration of gene variants and environmental factors to better understand the networks controlling complex traits [1]. The use of systems genetics approaches facilitates the connection of different trait networks with networks of gene and environmental variants, which helps in identifying the global genetic modulation of complex phenotypes [1]. Moreover, the integration of genotypic, expression, and other data within a Bayesian network framework has been shown to improve our mechanistic understanding of how DNA perturbations can induce changes in gene networks that ultimately affect disease outcomes [2]. This integrative approach provides a more detailed view of the polygenic and pleiotropic networks that modulate complex traits, such as those affecting the nervous system [5]. Overall, these advancements in network-based integrative genomics offer a more nuanced and interconnected view of complex trait architectures, moving beyond single-gene analyses to consider the broader network of interactions that contribute to phenotypic outcomes.", + "question": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_10 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_10 new file mode 100644 index 0000000..4ab901f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_10 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2015 - Quantitative and logic modelling of molecular and gene networks.pdf", + "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf" + ], + "extraction_id": [ + "d0102d97-2e08-50c3-86f4-d1103da9cca1", + "e23eae56-f71e-55fb-b443-e95adfe8ef22", + "2d776c48-9d99-5feb-9c18-113416c86d96", + "3292d5e1-b06c-5041-8190-44119ec0fdf0", + "f71776c8-e5c9-55e0-ad54-3725550dea19", + "452b1ade-c691-5feb-9a12-cfe83ae314af", + "b5c98115-372f-5bee-8517-80dc9b6838ee", + "c2a8a3ab-2531-55c1-920b-d908fa07c027", + "ae0e55f7-f33c-5179-ba14-8221c2a07be8", + "858f630f-9443-5f13-ac40-8e16eadd9ba1" + ], + "document_id": [ + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "8db6a373-be03-5653-beaf-1b2ae1d98c31", + "5ded506d-7935-53f9-a118-57a9f3943376", + "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "f64cf13c-d989-50da-be0d-81e34a735a42", + "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "f64cf13c-d989-50da-be0d-81e34a735a42", + "128224f1-3545-52c3-93cb-77c3cf4ec70a" + ], + "id": [ + "chatcmpl-AIGs9vl6ZxGFt8u7h4G1USup0nUIZ", + "83b84d63-4942-5c91-b93e-3ea1164c600e", + "05de9482-4937-5a26-b7fc-0a3cd86c4c40", + "b0b9c2ba-ff4b-5b2c-854f-70007eba8fd4", + "951c0969-df10-5038-b235-1bf4fa358ebb", + "09527834-da5e-5c34-9439-cf078f40870f", + "98fdd553-df98-510e-8e0d-62739abf5518", + "29e3d52a-5651-5cdc-94a7-babb6142e244", + "6bda096f-f5e1-51c9-9818-6c13cdfc8fe9", + "fbae4b79-573c-5b0b-ba0f-3761dbb22590", + "c63cfaee-749e-547b-9c0a-086266f10670" + ], + "contexts": [ + "genetic data which are shifting the paradigm of net work inferences by providing statistical evidence to support directed links betw een genes, proteins, metabolites or diseases. In Chapter 6 , different approaches using genetic data for gene network inference that have been proposed are reviewed. Chapter 7 examines the statistical potential of such methods under different realistic settings: varying population sizes and in the presence or absence of hidden factor var iation and suggests ways to", + "73. Yu,J., Smith,V.A., Wang,P .P ., Hartemink,A.J. & Jarvis,E.D. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 35943603 (2004). 74. Sachs,K., Perez,O., Peer,D., Lauffenburger,D. A. & Nolan,G. P . Causal protein signaling networks derived from multiparameter single cell data. Science 308, 523529 (2005). 75. Feizi,S., Marbach,D., Mdard,M. & Kellis,M. Network deconvolution as a general method to", + "Causal Inference of Regulator-Target Pairs by Gene Mapping 97 1.2 Background: Inferring Regula tory Networks from Correlated Gene Expression Independent of the data sets described so far, large collections of gene expres- sion over time course (Spellman et al., 1998) or varying environmental con- ditions (Gasch et al., 2000; Hughes et al., 2000) have been studied to reveal dependent variation among genes and thereby deduce regulatory relationships.", + "data, to infer possible pathways and help build a link from the phe-notype back to a causal gene. In many cases, such interaction data are already available in public archives and need not be generated anew by the researcher [ 1 ]. These different sources of interaction data can be collated into network models ( see Note 1 ) which allow analysis using techniques borrowed from graph theory.", + "relationships with a causal inference test . BMC Genet 2009, 10 :23. 60. Chaibub Neto E, Ferrara CT, Attie AD, Yandell B S: Inferring causal phenotype networks from segregating populations . Genetics 2008, 179 (2):1089-1100. 61. Li Y, Tesson BM, Churchill GA, Jansen RC: Critical preconditions for causal inference in genome-wide association studies under review 2010. 62. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient", + "T, Samson L, T I (2006) A systems approach to mapping DNAdamage response pathways. Science 312:10541059 Yu J, Smith V A, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks fromobservational biological data. Bioinformatics 20:35943603How to infer gene networks from expression proles M Bansal et al 10Molecular Systems Biology 2007 &2007 EMBO and Nature Publishing Group", + "with the data. To cope with this problem, Siegenthaler et al. proposed a novel assessment procedure that incorporates the inferability of gene regulatory interactions by redening the confusion matrix interms of inferability of the network, i.e., the possibility of the network to be determined from data. The inferability of GRNs was analyzed based on the causal information that could beextracted from experiments. Authors used data from the DREAM", + "and can thus be helpful in determining the causal structure of gene networks. Often, such data have already been gathered previously in the form of single-gene experiments and other links can be gleaned from a search of the published literature. In a few cases, a relevant database exists which can be used as a data source. Links of this type will all be directed edges from gene to phenotype (where the phenotype is the same as used as the seed).", + "tional methodologies in gene regulatory net-works. IGI Global, Hershey, PA, pp 127 11. Roy S, Das D, Choudhury D, Gohain GG, Sharma R, Bhattacharyya DK (2013) Causality inference techniques for in-silico gene regu-latory network, Mining intelligence and knowl-edge exploration. Springer, New York, pp 432443 12. Olsen C, Meyer PE, Bontempi G (2009) Infer- ring causal relationships using information the-oretic measures. In Proceedings of the 5th Benelux Bioinformatics Conference (BBC09)", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small" + ], + "answer": "The latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org involve several advanced methodologies. One approach is the use of Bayesian network inference, which has been advanced to generate causal networks from observational biological data [2]. This method allows for the modeling of probabilistic relationships between genes and can help infer causality from complex datasets. Additionally, there is a focus on using genetic markers to orient causal inference in genome-wide association studies, which is critical for understanding the genetic basis of phenotypes [5]. This involves identifying genetic variants that can serve as markers to infer causal pathways. Another strategy involves the use of network deconvolution, a general method to infer direct dependencies in networks, which can be applied to gene networks to clarify causal relationships [2]. Furthermore, the integration of multi-omics data and the use of updated tools on platforms like GeneNetwork.org enhance the ability to perform predictive medicine and systems genetics analyses, which are crucial for inferring causal relationships in gene networks [10]. These strategies collectively leverage statistical, computational, and biological insights to improve the inference of causal relationships in gene networks.", + "question": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_11 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_11 new file mode 100644 index 0000000..b92d6db --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_11 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2021 - New Technologies to Study Functional Genomics of Age-Related Macular Degeneration.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2020 - Integrative genomics approach identifies conserved.pdf", + "2023 - Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.pdf", + "2020 - The Genomics of Auditory.pdf", + "2016 - Single-cell genomics coming of age.pdf", + "2022 - Systems genomics in age-related macular degeneration.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2020 - Integrative genomics approach identifies conserved.pdf", + "2009 - Gene expression in the mouse eye an online resource for genetics using 103 strains of mice.pdf" + ], + "extraction_id": [ + "453f1ace-3591-50a3-afa5-86404632ace3", + "60355441-16f5-53a2-9b24-9616624f8d00", + "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "59e0781d-994c-5ef5-b2f4-073f4a73743b", + "16c769c7-b6ad-5b50-8d81-92c6768595f5", + "8d4d3a2d-0aca-5880-98e7-92638c72dd31", + "e488a94d-d7b3-5d56-bd56-95ac6e89d3ed", + "74048afb-68c3-520a-b661-1d347e9d2fcd", + "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "65c45e96-da39-59d8-9b9e-0679df8b1472" + ], + "document_id": [ + "419ee941-2cd6-56ae-8221-aed1c22a8ee2", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "8505ccf0-3138-5b83-b36d-8ebd7506a3a4", + "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "dca877e8-cbb9-561e-9b3c-6085228af97d", + "e8cf1e00-cf22-54cb-a0de-790a822c62d1", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "85241c56-1338-5b42-8b33-10b14514f169" + ], + "id": [ + "chatcmpl-AIGsGaW9DtpbrFAp6kQFqInDl6kUP", + "615beb0f-6b0a-59a6-a2fe-0be884c43d55", + "732b8fa8-8832-5002-bea1-bdde2bc61c64", + "849c1df7-4164-5164-b3be-6cdeb62ee555", + "3069c1d1-6b89-513a-83c3-e64cce07043f", + "504a960d-e669-52d1-b6c0-439b4f981d5f", + "769d2c00-d882-59a6-aa69-feb575c9fe1a", + "1fa406bc-fb29-5b60-90bc-1e77bd499df6", + "5f508353-ff30-5dfc-9bac-4bb8c6627391", + "42cf70a7-610a-5792-be62-58114dfc505a", + "908fad18-f471-5067-8bfc-f49951bdb4d1" + ], + "contexts": [ + "On the other hand, single-nucleus RNA-seq (snRNA-seq) provides an alternative method for gene expression proling in complex tissues from frozen samples at single cell levels (Grindberg et al., 2013). Compared to scRNAseq, snRNA-seq analyze gene expression within the nuclei instead of intact cells. It should be noted that there could be potential dierences between the RNA type and expression levels between nucleus and cytosol. As observed in a previous study comparing nuclear", + "most genetic and epigenetic mechanisms are yet to be probed with single-cell resolution. To understand the finer details at the level of a singular cell, sophisticated genomic and epigenomic next-generation sequencing (NGS) technologies have increased the potential for research output immensely (see Clark etal. 2018; Clark etal. 2016; Kelsey etal. 2017; Macaulay etal. 2017; Stuart and Satija 2019). These would", + "of the disease, profiling gene expression in only bulk tissue sam-ples may obscure biologically relevant cell-type specific changes. While single-cell RNA-seq allows us to evaluate transcriptional changes within cell-types, it is prohibitively costly to executeon large cohorts (i.e. hundreds of individuals). To circumvent this issue, we developed a framework that leverages single-", + "2019). The traditional RNA sequencing technology (bulk RNA-seq) is applied to determine gene expression pro les, isoform expression, alternative splicing and single-nucleotide polymorphisms on basis oftissue samples, which contains various cell types ( Kuksin et al., 2021 ). On the contrast, single-cell RNA sequencing (scRNA-seq), a noveltechnology can detect the gene expre ssion patterns for each transcript within single cell and distinguish cell subtypes ( Lhnemann et al., 2020 ).", + "sion from smaller amounts of RNA enabled cell typespecific analyses.Specific cell types can beisolated using flow cytometry, for example, using endogenously expressed fluorescent markers, with or without combining with antibodies for cell surface proteins. Transcriptomic analysis by either microarray or bulk RNA sequencing then follows (39,67,68,104,145).Such analyses can 280 Taiberetal. Annu. Rev. Genom. Hum. Genet. 2022.23:275-299. Downloaded from www.annualreviews.org", + "Recent applications Single-cell RNA sequencing has had a profound impact on our understanding of neuronal and hematopoietic cell types, as well as the immune system. Examples of novel insights in immunity include a window on to an unexpected plethora of dendritic cells in mouse immun- ity [25] and new regulators and subpopulations of CD4+ T cells [26 28]. In hematopoiesis, much single-cell tran- scriptomics work has focused on hematopoetic stem cells and the single-cell perspective has provided reso-", + "single- nucleus RNAseq makes them a valuable complement to the find- ings published by Orozco, Chen et al. (Orozco et al., 2020 ). Furthermore, Yan et al. (2020) used cell sorting to enrich for cell types with a high degree of heterogeneity, resulting in finer cell subtype resolution for non-photoreceptor cell types such as RGCs. In addition to neural retina, our understanding of the choroidal", + "using sequencing (ATAC-seq),95,96 that can map chro- matin interactions and accessibility with higher resolu-tion than previous methods will improve our ability to disentangle GWAS loci; while single-cell RNA sequenc- ing 97,98 and CRISPR-based pooled gene perturbation methods99103 provide unprecedented opportunities for studies of how RNA expression patterns differ between cells within tissues and how those tissues and cells react to perturbation of multiple genes in parallel.", + "cell RNA-seq data from a smaller cohort in conjunction withco-expression network analysis in order to estimate cell-typespecific transcriptomic changes in large, bulk tissue RNA-seq datasets. We isolated nuclei and performed single-nuclei RNA-seq (snRNA-seq, n= 27 321 nuclei) on postmortem human brain tissue from aged, neurologically healthy controls ( n=5 ,6 7t o9 0 + years old, PFC, Supplementary Material, Table S1 ) to clarify cell- type proportions and the corresponding transcriptional profiles", + "without the biases of probe sequence selection and hybridization reactions. The second innovation is cell-specific RNA profiling methods [79] that make it practical to generate comparatively accurate expression data for individual cell types in genetically engineered lines of mice. We can soon expect far more comprehensive and specific lists of genes for several important cell and tissue types that can be used to assemble multicellular expression networks in eye.ACKNOWLEDGMENTS Dr. Eldon E." + ], + "answer": "Advancements in single-nucleus RNA sequencing (snRNA-seq) provide more granular insights into cell-type-specific gene expression networks by allowing for the analysis of gene expression within the nuclei of cells, rather than relying on intact cells as in single-cell RNA sequencing (scRNA-seq) [1]. This method is particularly useful for profiling gene expression in complex tissues from frozen samples at the single-cell level, which can be challenging with other techniques [1]. Additionally, snRNA-seq can help clarify cell-type proportions and corresponding transcriptional profiles, as demonstrated in studies involving postmortem human brain tissue [9]. By isolating nuclei and performing snRNA-seq, researchers can achieve finer cell subtype resolution, which is crucial for understanding the heterogeneity within cell populations [7]. This level of detail is essential for constructing accurate cell-type-specific gene expression networks, as it allows for the identification of transcriptional changes and cell-type-specific gene expression patterns that might be obscured in bulk tissue analyses [3]. Overall, snRNA-seq enhances our ability to dissect complex tissues into their constituent cell types and understand the unique gene expression networks within each type, providing a more detailed and nuanced view of cellular function and interaction.", + "question": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_12 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_12 new file mode 100644 index 0000000..1bbd0b6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_12 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2009 - Genes and gene expression modules associated with caloric.pdf", + "2009 - Visual analytics for relationships in scientific data (1).pdf", + "2007 - Integrating physical and genetic maps from genomes to interaction networks.pdf", + "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf", + "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf" + ], + "extraction_id": [ + "29c980ec-bd6e-5ae7-a61c-5abd67d0ef67", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "c0983224-1ade-5c10-9f2b-847e9b33f706", + "56129761-d500-59b9-bd9b-cd9cbcada21c", + "d64d8cf5-5b57-5a29-99b4-a8d2ab4bda21", + "ba1a83a3-d0e9-5f1e-870f-228abdae771d", + "298ee1f5-58a9-567c-86ba-8ac5967e1718", + "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "9c89683f-aca5-57f9-b28d-62e9eb64377b" + ], + "document_id": [ + "17264155-b665-59db-94cb-f4d67eac20fc", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "893ba204-2e69-563f-9046-7246ca61494f", + "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "a9a113e2-d5e5-5903-91de-4b45b37d870f", + "27c922c6-e449-5f83-868a-3ad7284facc8", + "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101" + ], + "id": [ + "chatcmpl-AIGsO45INZIWjU37FcOiRroinBDZj", + "302feae2-3bab-5fb8-8483-0cea906c83e8", + "c63cfaee-749e-547b-9c0a-086266f10670", + "0374a059-20c1-5b75-a7a7-bf69ce03740c", + "860be786-e27d-5dd1-96bf-4bcc48957b4d", + "4488c0f4-c24a-5b6d-814a-a30b15cc4c03", + "9f6fb84a-f487-5ea6-a84e-403642b6d76e", + "0858b8f7-66f3-5741-ae7e-4504bca7292f", + "a02b4589-65ec-50e1-9849-090971ddb2b0", + "7d3e3705-c5e7-5a37-91c1-a87842f5b9a7", + "73198d17-f9ce-5528-89d8-f6e466258708" + ], + "contexts": [ + "52.Zhu J et al. (2007) Increasing the power to detect causal associations by combining genotypicand expression data in segregating populations. PLoS Comput Biol 3:e69 53.Zhu J et al. (2008) Integrating large-scale functional genomic data to dissect the complexity ofyeast regulatory networks. Nat Genet 40:854861 54.Kim JK et al. (2005) Functional genomic analysis of RNA interference in C. elegans. Science308:11641167", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "expression and its effect on disease . Nature 2008, 452 (7186):423-428. 12. Chen LS, Emmert-Streib F, Storey JD: Harnessing naturally randomized transcription to infer regulatory relationships amo ng genes . Genome Biol 2007, 8(10):R219. 13. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient the edges in quantitative trait networks: the NEO s oftware . BMC Syst Biol 2008, 2:34. 14. Millstein J, Zhang B, Zhu J, Schadt EE: Disentangling molecular", + "and unknown function by large-scale coexpression analysis. Plant Physiol 2008, 147:41-57. 98. Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals gen- eral applicability of \"guilt-by-a ssociation\" within gene coex- pression networks. BMC Bioinformatics 2005, 6:227. 99. Lee NH: Genomic approaches for reconstructing gene net- works. Pharmacogenomics 2005, 6:245-58. 100. Goutsias J, Lee NH: Computational and experimental approaches for modeling ge ne regulatory networks. Curr", + "the discovery of interface genes. These mRNA transcripts regulate expression of genes in those structures, and thereby couple multiple networks a nd biological processes. The detection of these transcripts and the analysis of their gen es regulatory polymorphisms 37", + "Rev. Genet 2007;8:437449. [PubMed: 17510664] A review of theory and approaches to mapping genetic interaction networks. 16. Bork P, et al. Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol 2004;14:292 299. [PubMed: 15193308] 17. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 1998;8:175185. [PubMed: 9521921]", + "CC represents a dramatic improvement over existinggenetic resources for mammalian systems biology appli- cations (Adam et al. 2007 ; Chesler et al. 2008 ). A number of gene expression data sets from microarray experiments,particularly those for mouse and rat, have been integrated into GeneNetwork ( http://www.genenetwork.org ), which is essentially a web knowledgebase in which the entire dataset and relevant metadata (data about the data) are com- bined with sophisticated statistical and computation tools", + "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", + "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human", + "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human" + ], + "answer": "The context provided does not explicitly mention the impact of recent discoveries in non-coding RNA regulation on refining gene interaction maps on GeneNetwork.org. However, there are some relevant points that can be inferred: 1. GeneNetwork.org has been updated with new tools and data, including multi-omics analysis, which could potentially incorporate non-coding RNA data to enhance gene interaction maps [2]. 2. The integration of gene expression data sets, particularly for mouse and rat, into GeneNetwork.org suggests that the platform is equipped to handle complex genetic data, which may include non-coding RNA interactions [7]. 3. There is ongoing research and data collection on non-coding RNAs, as indicated by references to databases like Rfam and cisRED, which could contribute to refining gene interaction maps by providing insights into regulatory networks [9], [10]. While the specific impact of non-coding RNA discoveries on GeneNetwork.org is not detailed, the platform's enhancements and the broader research context suggest that such discoveries could play a role in improving the accuracy and depth of gene interaction maps.", + "question": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_13 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_13 new file mode 100644 index 0000000..d86834d --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_13 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "2015 - Identification of candidate genes that underlie the QTL on chromosome 1 that mediates genetic differences in stress-ethanol interactions.pdf", + "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2012 - Aging effects on DNA methylation modules.pdf", + "2016 - Alterations in the expression of a neurodevelopmental gene exert long-lasting effects on cognitive-emotional phenotypes and functional brain networks translational evidence from the stress-resilient Ahi1 knockout mouse.pdf", + "2018 - Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis.pdf", + "2019 -Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "26045fea-cd20-5e3d-be07-e8a8e9ca603a", + "f1181fc1-fe08-53b1-bda7-00423a568234", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "bf37d9e2-c9a3-5886-88db-103264c4cecb", + "ea5fd027-559f-568f-9c4d-a4615730426a", + "434963e5-549e-5986-90a9-cbf4a5f7f06e", + "dab0ce13-0d90-514c-9220-8edd64eceb6c" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "eecf4236-efca-577d-ba62-c20c9768950e", + "d9038328-bfea-5f73-87aa-6077b697e4db", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "cdd3bf57-3c36-5673-bd78-1e53f384d539", + "8cd3e767-17b8-5868-b335-fdb6cc2ff02c", + "e4b2f5dc-6df6-5af8-9ca9-3ccb6518d300", + "0a22eed8-cdda-52de-a73f-d82b3f73b78d" + ], + "id": [ + "chatcmpl-AIGsUMf0eTPsxD8TBs1unBQPLXIKg", + "c63cfaee-749e-547b-9c0a-086266f10670", + "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "6beb1115-9f40-555f-a6b4-3c73945101a0", + "6e2695ed-e652-52e1-b896-0bbbb585bb60", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "bd4b772b-4df4-588e-a7bd-2d5d9484f945", + "9bf34d9a-9c54-5376-a38e-7f32daba8107", + "225f0aa2-c185-5b36-923a-a24e545b866f", + "b6b401f6-66c1-5e0d-ab68-09f6f6d7e10f" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", + "GeneNetwork, a public web source used to study relations amongmarkers, genes, and phenotypes. We made use of large transcriptomedata sets for the amygdala, hippocampus, ventral tegmental area", + "ject to mapping analysis. We examine the connectivity among these sets and analyze the molecular, biochemical and genetic regulatory commonality of connected genes us-ing novel and existing bioinformatics tools. We also develop data-driven hypotheses to explain the mechanisms of genetic perturbations and variation as a means of dening global consequences of individual differences on tissue structure and function. Much of our work is motivated by prior studies of brain gene expression and mRNA", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "weighted gene co-expression network are described in[54]. Consensus network analysis was carried out with Rfunction blockwiseConsensusModules in the WGCNA R package [54]. Our online R software tutorial easily permits the user to identify tissue-specific age related modules and CpGs. Gene ontology enrichment analysis", + "approach employed in the construction of large expression data sets, such as those provided by GeneNetwork,39treats gene expression as a continuous variable across RI strains, rather than asa categorical one (knockout model). Hence, we believe that using these complementary, yet conceptually distinct, approaches enhanced our ability to propose mechanistic insights. A limitation of the current study relates to the non-trivial relationship between structural and functional brain connectivity.4", + "GeneNetwork ( http://www.genenetwork.org ; Williams and Mulligan, 2012)). These databases 180 represent transcriptome datasets for different tissues of recombinant inbred mice. If several probes 181 for the same gene were available, probes with higher maximum likelihood ratio statistic (LRS, a 182 measurement of the association or linkage between differences in traits and differences in particular 183 genotype markers values) were used. 184", + "pathways.TheGeneNetworkdatabaseisauniqueresourceforco-expressionanalysisusingdatafromavarietyof tissues acrossgeneticallydistinctinbredmice.However,extractionofbiologicallymeaningfulco-expressedgenesets ischallengingduetovariabilityinmicroarrayplatforms,probequality,normalizationmethods,andconfounding biologicalfactors.Inthisstudy,wetestedwhetherliteraturederivedfunctionalcohesioncouldbeusedasanobjectivemetricinlieuofgroundtruthtoevaluatethequalityofprobesandmicroarraydatasets." + ], + "answer": "The provided context does not explicitly mention the integration of spatial transcriptomics approaches into GeneNetwork.org. However, it does describe GeneNetwork as a resource for systems biology and systems genetics, which includes large transcriptome datasets from multiple tissues [2], [9]. The platform is used to study relationships among markers, genes, and phenotypes, and to analyze genetic regulatory commonality and tissue structure and function [3], [4]. While spatial transcriptomics is not directly referenced, the existing capabilities of GeneNetwork in handling multi-omics data and performing systems genetics mapping [1], [5] suggest that it could potentially support spatial transcriptomics approaches to enhance understanding of tissue architecture and function.", + "question": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_14 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_14 new file mode 100644 index 0000000..113f7a8 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_14 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "2009 - Genetics of the hippocampal transcriptome in mouse a systematic survey and online neurogenomics resource.pdf", + "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf", + "2015 - Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross.pdf", + "2008 - Type 2 diabetes new genes, new understanding.pdf", + "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "2022 -Chunduri- Drugs Animal Models.pdf" + ], + "extraction_id": [ + "16fdf35c-ab83-53db-9f76-e817326c6067", + "76e22011-da6d-5af7-a74f-2b4d0f11e879", + "957166a3-0298-5324-a24a-02b59ec3427f", + "a47731b3-bb43-5d9c-a7eb-bfea5eea557e", + "47c06e52-1923-58d0-9286-9674893a502a", + "3296b30e-7dd3-576d-a2df-442406caa472", + "121f6744-a773-5a59-b8c7-7e7e85e2b067", + "31a1546b-c160-5b22-a3fb-1e26ab2861c3", + "9b24b22c-0f8c-5b3c-9479-518aa6dfab01", + "b93f76a6-6928-564a-a9a0-149c8ef774d9" + ], + "document_id": [ + "de8dda5e-0e2f-5aa9-bb13-851c526b36a5", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "33437cc7-ee4e-59b9-b3e6-ed50eb504b52", + "d9038328-bfea-5f73-87aa-6077b697e4db", + "3d0df5a3-7d7c-5edc-b94d-cae582f59c12", + "464d119c-ba16-5716-8b69-169940f090b4", + "3992c53c-c48c-597d-8d96-70b1026deb70", + "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45" + ], + "id": [ + "chatcmpl-AIGsZPO5vngTlUtdx2Hky3vN1AZqv", + "93d79fcd-e841-5cd0-baa7-69ad032cacef", + "248ac859-2589-5afa-824d-e1357bf23e59", + "32497309-fb89-58c9-b828-6a16fa55c11d", + "ea5eaca1-c91d-59f6-af5b-5490749d950a", + "56ba9ce8-4cdd-5d4d-83c1-a370e9c8f959", + "cd33f83f-d19c-5419-a157-c2f1d8148347", + "e5354b88-c1ec-54e1-ab61-c30689e30ea1", + "3fa64113-fa70-575c-81ae-0769dff93a27", + "662c7b64-e34e-5faa-b920-6b59334ef372", + "f9ca5851-0871-54ae-8d01-752c806bd081" + ], + "contexts": [ + "to as quantitative trait loc us (QTL) mapping study. QTL studies inform us region s on the chromosome where existing polymorphisms or SNPs are highly correlated with variation of the trait of interest. With the advancement in DNA sequencing, whole genome database of several mouse strains as well as gene expression data from several tiss ues are available. This allows us to use bioinformatic tools to identify candidate genes with greater confidence for further functional validations .", + "differences, allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation. QTL mapping of gene expression traits allows us to identify eQTLs; genomic regions that have a regulatory effect on those expression traits. Two types of eQTLs can be distinguished, i.e., those that map near (less than 10 Mb from) the gene which encodes the transcript (local ) and those that map elsewhere in the genome ( distant ). 18 Together, local", + "simultaneously. Beginning with a study in yeast (Brem et al. 2002), QTL mapping has been done with gene expression as the phenotype. In such a study, the genomic loci responsible for variation in gene expression can be used to infer regulatory control. While such a study is not conclusive, it can be used to narrow the potential regulatory candidates, generate hypotheses for further testing and construct regulatory networks in s ilico.", + "is that one can now identify large numbers of less strong, second-ary QTLs which were previously lost to background noise, and this information opens up a whole new range of possible analy-ses, such as the identi cation of epistatic interactions ( Figure 5), that promise to uncover pathways of genetic control within the tissue studied. Traditionally, QTL mapping starts with a phenotype of inter-", + "and quantitative trait loci (QTL) regulatory models. A major goal is to identify which,among a set of candidate genes, are the most likely regulators of trait variation. These methods are applied in an effort to identify multiple-QTL regulatory models for large groups of genetically co-expressed genes, and to extrapolate the consequences of thisgenetic variation on phenotypes observed across levels of biological scale through the", + "distal regions into even finer regulatory loci. This influence on gene expression may be the reason why so many classical QTLs have been mapped to Qrr1 . The complexity highlighted by Qrr1 may very well be the rule rather than the exception for loci that modulate complex traits. Efforts to fine -map a single QTL have often been confronted by clusters of multiple small effect QTLs within the original interval (Legare et al., 2000; Demarest et al., 2001) . This poses a serious challenge, and", + "genotypes, availing of genetic markers across the whole genome, and allow the identication of QTLs with signi- cant effects on the disease (Darvasi 1998 ; Manolio 2010 ). QTLs are genetic regions closely linked to a gene with a quantitative effect on the phenotype. QTL mapping is based on the concept that phenotypic differences between inbred mouse strains can be used to demonstrate theimportance of genetic effects on complex phenotypes (Andreux et al. 2012 ; Hillebrandt et al. 2002 ). The standard", + "of the variants within associated loci through expression-quantitative trait locus (eQTL) studies will combine the genetic variation in associate d loci with expression analysis data to define regulatory relationships. Studies designed to understand the functional effect of any causal variants in relevant cell systems and an imal models will give insight to physiological consequence. These advances will underpin efforts to translate the findings through development of diagnostic tests, ris k evaluation and", + "illustrating the potential of functional mapping for effici ently establishing associations between existing QTL, as well as for novel QTL discovery. References 1. Damerval C, Maurice A, Josse JM, De Vienne D: Quantitative trait loci underlying gene product va riation: a novel perspective for analyzing regulation of genome expression. Genetics 1994, 137:289-301. 2. Brem RB, Yvert G, C linton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science 2002, 296:752-755.", + "over a decade ago, using new genometypes for the BXD family of murine strains, as well as new statistical tools, showing that we can identify new quantitative trait loci (QTLs), resulting in highly plausible candidate genes. Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [ 3]). Rodents, especially mice, have been the species most prominently used for biomedi-" + ], + "answer": "Recent developments in quantitative trait locus (QTL) mapping have significantly refined our understanding of gene regulatory variants in several ways: 1. **Identification of eQTLs**: QTL mapping of gene expression traits allows for the identification of expression quantitative trait loci (eQTLs), which are genomic regions that have a regulatory effect on gene expression traits. These eQTLs can be categorized into local eQTLs, which are located near the gene encoding the transcript, and distant eQTLs, which are located elsewhere in the genome [2]. This distinction helps in understanding the regulatory architecture of the genome. 2. **Increased Resolution and Confidence**: With advancements in DNA sequencing and the availability of whole-genome databases and gene expression data from various tissues, researchers can use bioinformatic tools to identify candidate genes with greater confidence for further functional validations [1]. This enhances the precision of QTL mapping in pinpointing regulatory variants. 3. **Functional Mapping and Hypothesis Generation**: QTL mapping studies, such as those beginning with yeast, have used gene expression as the phenotype to infer regulatory control. Although these studies are not conclusive, they help narrow down potential regulatory candidates, generate hypotheses for further testing, and construct regulatory networks in silico [3]. 4. **Detection of Secondary QTLs and Epistatic Interactions**: Recent developments allow for the identification of large numbers of less strong, secondary QTLs that were previously obscured by background noise. This opens up new possibilities for analyses, such as identifying epistatic interactions, which can reveal pathways of genetic control within the studied tissue [4]. 5. **Integration with Expression Analysis**: The integration of genetic variation in associated loci with expression analysis data through eQTL studies helps define regulatory relationships. This approach provides insights into the physiological consequences of causal variants, aiding in the translation of findings into diagnostic tests and risk evaluation [8]. Overall, these advancements in QTL mapping enhance our understanding of the complex regulatory mechanisms underlying gene expression and trait variation.", + "question": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_15 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_15 new file mode 100644 index 0000000..5dbdfbc --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_15 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2016 - Next Generation Transcriptomics.pdf", + "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf", + "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "2007 - Bioinformatics_for_Geneticists.pdf", + "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2011 - Human genetics and genomics a decade after the release of the draft sequence of the human genome.pdf", + "2010 - Genome-wide analysis of histone modifications.pdf", + "2011 - Molecular Genomic Research Designs.pdf" + ], + "extraction_id": [ + "b3a2e390-fad2-5afc-b8ca-6f0b57b9cac1", + "203710b7-3267-5ecf-9397-b5becdaeead1", + "767fd341-f407-5322-a932-9b1cecb869e0", + "9515bd16-96d4-5b09-b23c-63a1cc5d19ae", + "522373ca-3ce6-5fe5-b062-ee097f378397", + "c5468773-a09b-510d-bcdf-f685d7714106", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3960aec4-df25-57cd-9c60-5561f876a795", + "6b5317f7-aa3f-5dfe-8e50-ef90619b6707", + "d3fe612e-6d4a-5410-9e60-cd2ef8fff897" + ], + "document_id": [ + "56cafe26-2b36-50d6-a5c7-c7d947473b61", + "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "af6e0103-849d-542f-bca7-0251082bc0b3", + "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "045edae8-468b-5725-be06-8cb4b8f6a92b", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "6d475ac7-7094-5268-96ce-ae8f50f42cd2", + "68bfce04-818d-5122-91c2-13a4a3ba0229", + "ced08e27-8655-59a4-bf63-0ba746f139b7" + ], + "id": [ + "chatcmpl-AIGsiGGBCVBLOOrTQrnGlB4EM7iVd", + "a87fa6ff-4bc0-50ac-b654-f7d734bbbf02", + "66fa4c5e-0b26-5c01-b5ec-d199a4da11bb", + "77ae8cce-6686-5930-a6a1-291143cba4c5", + "9c31e888-0660-507d-927f-e54f98a7248f", + "5935ee2f-4621-577d-8d9b-e47d2d0699e2", + "0f00daa0-2bb4-5a3f-8d51-a1cd2957bef4", + "c63cfaee-749e-547b-9c0a-086266f10670", + "03e25c07-34a0-5b1f-a5f9-ba9a0e2c0d91", + "2e2d861b-4662-5ba5-80e6-ff0e4d9e80b4", + "47eea0dd-b899-5ed2-8b16-150b976f1f0a" + ], + "contexts": [ + "frequent usage of terms like epigenetic or chromatin land-scape. New methods for high-throughput mapping ofgenome-wide histone modifications and protein-DNA inter- actions were developed over the last few years (Blecher-Gonen et al., 2013; Garber et al., 2012). Histone Modifications Associated with Gene EnhancersChromatin can be modulated by covalent histone modifica-", + "orative efforts of the ENCODE Project [ 42] and Roadmap Epigenomics [ 43] consortia have already revealed a compendia of genome-wide histone modification signatures for various regulatory features in multiple primary tissues and cell lines. These datasets have been applied to global mapping studies and databases to prioritize functional regula- tory variants [ 44,45]. While these assays have been employed extensively in LCLs, and tumor cell lines to follow-up auto-", + "genetical genomics) and the genetics of epigeneticscould be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such anapproach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations. The level of complexity could be further increased by including different cell types in the analysis, such as the", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "374. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F ., Ren, B. et al. (2010), The NIH Roadmap Epigenomics Mapping Consortium, Nat. Biotechnol. V ol. 28, pp. 10451048. 375. Portela, A. and Esteller, M. (2010), Epigenetic modications and human disease, Nat. Biotechnol. V ol. 28, pp. 10571068. 376. Esteller, M. (2007), Cancer epigenomics: DNA methylomes and histone-modication maps, Nat. Rev . Genet. V ol. 8, pp. 286298. 377. Gilad, Y ., Rifkin, S.A. and Pritchard, J.K. (2008), Revealing the archi-", + "likely to be part of regulatory elements. Our global map of histone marks will serve as an important resource forunderstanding the epigenetic basis of type 2 diabetes. [Supplemental material is available online at http:/ /www.genome.org. The ChIP-seq and gene expression data from this study have been submitted to ArrayExpress (http:/ /www.ebi.ac.uk/microarray-as/ae/) under accession nos. E-MTAB-189 and E-MTAB-191, respectively.] Genetic and epigenetic factors determine cell fate and function.", + "these with other epigenetic mechanisms. This section will describe each method and provide the reader with technologies and recommendations to aide in the design and implementation of an epigenetic study . Histone Modifi cation Analysis Histone modi cation signals can be captured with chromatin immunoprecipita- tion (ChIP), which provides modi cation position approximation on the genome" + ], + "answer": "Incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org has several implications: 1. **Enhanced Functional Analysis**: By integrating epigenetic data, the predictive functional analysis of SNPs can be improved. This is because epigenetic data can highlight regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory proteins, which can affect gene expression and regulation [4]. 2. **Prioritization of Regulatory Variants**: The incorporation of genome-wide histone modification signatures, as revealed by collaborative efforts like the ENCODE Project and Roadmap Epigenomics, allows for the prioritization of functional regulatory variants. This can be particularly useful in mapping studies and databases, enhancing the understanding of regulatory features in various tissues and cell lines [2]. 3. **Understanding Gene Expression Variation**: By studying the genetics of epigenetics, it is possible to reveal genes that directly or indirectly affect epigenetic gene states. This approach can help estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations, thus providing a deeper understanding of gene regulation [3]. 4. **Resource for Predictive Medicine and Systems Genetics**: The integration of multi-omics data, including epigenomic data, makes GeneNetwork.org a valuable resource for predictive medicine and systems genetics. This integration supports more comprehensive analyses and enhances the platform's utility for research and clinical applications [7]. Overall, incorporating epigenomic data into GeneNetwork.org enriches the platform's analytical capabilities, offering deeper insights into gene regulation and expression, and supporting advanced research in genetics and epigenetics.", + "question": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_16 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_16 new file mode 100644 index 0000000..c056ae4 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_16 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2015 - Insights into Sex Chromosome Evolution and Aging from the Genome of a Short-Lived Fish.pdf", + "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2009 - Rare Variants of IFIH1, a Gene Implicated in Antiviral Responses, Protect Against Type 1 Diabetes.pdf", + "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "2009 - Loss of A-type lamins and genomic instability.pdf", + "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf" + ], + "extraction_id": [ + "516bdde0-cafe-5eac-a627-32ef9e262e32", + "9309edf2-5e2d-5567-ae78-f6681b866410", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "5b8b3673-7fd4-5989-9982-a6d5ea374c8d", + "46616368-74e6-5605-9e43-9789e8e1bea1", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "9893879f-6b73-5dc6-b274-f48ba8163644", + "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "a6f16fce-1813-5d38-899f-6eb04c7d0007", + "f4955281-f174-562e-bc8a-170b701beffc" + ], + "document_id": [ + "def32424-2f9d-5c4b-9c03-be2d8bd53a24", + "6acebf19-b80c-5352-8201-99d5634fcc80", + "17264155-b665-59db-94cb-f4d67eac20fc", + "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "17264155-b665-59db-94cb-f4d67eac20fc", + "7d9d5ce7-7bfe-5fe2-a325-fe97db015a10", + "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "eab22335-5688-5e37-9f65-c4b58d6d95c8", + "51350055-d53c-5692-ab53-337b8a8bafd6" + ], + "id": [ + "chatcmpl-AIGsp8i4dh5GAWf5RuA0Au0DHwVdV", + "996bbbd1-c605-5733-bf20-42367be2244c", + "c7834fbb-eb9a-5f55-ac26-67af248e7179", + "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "5543c99f-6542-55b8-b62c-e34a03b9c2fe", + "33dc52df-73a5-514e-8edb-33ae5046b8af", + "e26ebc1e-e05d-56fb-8718-604275994a84", + "fb3452e6-4584-5c3f-92de-9bc44d30b21c", + "a02b4589-65ec-50e1-9849-090971ddb2b0", + "38405f05-93df-579e-a8e4-c6b0c13e86a6", + "67365e1f-c588-56b5-aae0-44604958f8e1" + ], + "contexts": [ + "genomes. Hence, chromosomal and spatial co-localization in the nucleus may indicate co-regulation. It was previously shown that 3D chromatin structure couples nuclear compartmentaliza-tion of chromatin domains with the control of gene activity ( Gue- len et al., 2008 ) and thus contributes to cell-specic gene expression ( Zullo et al., 2012 ). In this context, it is noteworthy that cellular senescence is associated with modications of theglobal chromatin interaction network ( Chandra et al., 2015 ). To", + "2 Introduction Recent scientific advances have enabled the identification of functional genomic elements through a diverse set of functional annotations, including proteins functional scores (1, 2) , evolutionary conservation scores (3-5), and epigenetics scores from the Encyclopedia of DNA Elements (ENCODE) (6). Other initiatives such as the R oadmap Epigenomics project (7) and FANTOM5 project (8, 9) also provide evidence for potential regulatory v ariants in the human", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "a growing awareness that the three-dimensional juxtaposition of DNAregions within nuclei means that genes can be regulated by regulatory elements that are located at some distance from the gene ( Fig. 5 ) (Javierre et al., 2016 ;Kadauke and Blobel, 2009 ). As a result of this, disease associated SNPs have been shown to fall in gene regulatory elements ( Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014; Lee et al., 2014; Schierding et al., 2015 ).", + "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", + "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", + "regions correlated with functional noncoding elements, including enhancers, better than did regions identified solely on the basis of nucleotide sequence. These results support the idea that the molecular shape of DNA is under selection and can identify evolutionary history. Genomic sequences that code for proteinsare relatively well understood but make up only ~2% of the human genome ( 1). Many functions are encoded in the remaining ~98% noncoding portion of the genome, but little", + "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", + "References 1. Cremer T, Cremer M, Dietzel S, Muller S, Solovei I, Fakan S. Chromosome territoriesa function-al nuclear landscape. Curr Opin Cell Biol 2006; 18:307-16. 2. Misteli T. Beyond the sequence: cellular organization of genome function. Cell 2007; 128:787-800. 3. Schneider R, Grosschedl R. Dynamics and interplay of nuclear architecture, genome organization and gene expression. Genes Dev 2007; 21:3027-43.", + "enhancers in the control of cell identity and disease. Cell(2013) 155:934 47. doi: 10.1016/j.cell.2013.09.053 45. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev (2012) 26:11 24. doi: 10.1101/gad.179804.111 46. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol (2016) 17:743 55. doi: 10.1038/nrm.2016.104 47. Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, et al." + ], + "answer": "Recent findings on 3D genome organization have significantly enhanced our understanding of functional genomic networks in several ways: 1. **Co-regulation through Spatial Organization**: The 3D chromatin structure is known to couple nuclear compartmentalization of chromatin domains with the control of gene activity, which contributes to cell-specific gene expression [1]. This spatial organization within the nucleus suggests that chromosomal and spatial co-localization may indicate co-regulation of genes, thereby influencing functional genomic networks. 2. **Regulation by Distant Elements**: There is a growing awareness that the three-dimensional juxtaposition of DNA regions within nuclei allows genes to be regulated by elements located at a distance from the gene itself [4]. This understanding helps explain how disease-associated SNPs can fall within gene regulatory elements, thus affecting genomic networks and potentially leading to disease. 3. **Integration with Functional Annotations**: Advances in identifying functional genomic elements through various annotations, such as those from the ENCODE project, have been complemented by insights into 3D genome organization. This integration helps in identifying potential regulatory variants and understanding their roles within genomic networks [2]. These findings collectively contribute to a more comprehensive understanding of how genes are regulated within the complex spatial architecture of the genome, thereby enhancing our knowledge of functional genomic networks.", + "question": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_17 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_17 new file mode 100644 index 0000000..831f26c --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_17 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "2011 - Annotating individual human genomes.pdf", + "2007 - Classification of microarray data using gene networks.pdf", + "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "2019 - Systems genetics approaches to probe gene function.pdf", + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf" + ], + "extraction_id": [ + "53c57cc4-4d43-505a-974c-442d06e144df", + "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "223e442e-898d-5aea-866a-5cdc0ac915e8", + "070421c2-5d23-58b3-9d85-53dd58e7abae", + "df700ffb-556a-5331-afe6-71f7e77a1fb8", + "c15261b7-54b9-534f-ac95-17c7a5543f31", + "f46459a1-592e-5d14-a6d1-f93211353db0", + "29c89d19-3215-54dc-9723-85f96de02b65", + "d4d71d8c-ef2f-5ddb-b3f3-0f5ce8dc0a83", + "3276b251-2e60-53e8-8fd1-07702f486a43" + ], + "document_id": [ + "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "ef974b09-4ea2-5382-85e5-c2169f440fda", + "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", + "639e0456-a445-5e2e-adf5-8eaf987ce2d1", + "f64cf13c-d989-50da-be0d-81e34a735a42", + "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "17264155-b665-59db-94cb-f4d67eac20fc" + ], + "id": [ + "chatcmpl-AIGsxUUcXG8q6ZckzX5v3uoIBTYQl", + "df726361-271a-5dbb-b6d1-03dab5a63006", + "ee9014b2-ff70-50d1-a022-7a5792383700", + "6d8b4af6-6baf-58ff-9e1d-003862f53edd", + "e8279254-6a66-5be6-b6ae-c11c20e242f9", + "137c8fc7-7bc2-543f-a43e-7f819eaaaaa9", + "394f5f79-0592-52ff-bc83-ea55a95fd17e", + "b54b5584-344c-54e5-9442-a7deb099bc76", + "09f8c37f-b150-5f07-8275-bd040787f514", + "3152b693-2396-5441-b6ff-6a80eac13ad0", + "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1" + ], + "contexts": [ + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "Here we will focus on gene network inference algorithms (the inuence approach). A description of other methods based on the physical approach and more details oncomputational aspects can be found in (Beer and Tavazoie,2004; Tadesse et al, 2004; Faith and Gardner, 2005; Prakash and Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al, 2006). We will also briey describe two improper reverse-engineering tools (MNI and TSNI), whose main focus is not", + "NIA[360] may help to infer a putative function by linking unkn own genes to genes known from previous studies to show a similar e xpres- sion pattern. We can also characterize unknown genes by thei r evolu- tionary, loss-of-function and network interaction proper ties to prioritize candidate variants[184] and even predict disease inherita nce mode to a certain degree[153]. Taking this approach a step further, GeneNetwork[99] is con structed", + "network inference techniques can be utilized to infer biologicalprocess and the potential phenotypic impact of variants in genes of unknown function [71 78]. Thus, pathway and network based annotation approaches can be powerful approaches to inferring phenotypic information where direct links to phenotype do not exist. 2.12. De novo association analyses involving multiple genomes In the absence of prior information one might leverage to annotate", + "interaction may be difficult to quantify. Conversely the directions and signs that accompany signalling or regula- tory pathways are generally known, but their incorpora- tion requires more work. It could nevertheless lead to important advances for the interpretation of microarray data in cancer studies, for example. Conclusion We have presented a general framework to analyse gene expression data when a gene network is known a priori . The approach involves the attenuation of the high-fre-", + "A number of techniques have been proposed for network inference. Existing techniques for nding gene networks can be broadly cate-gorized as (i) computational approaches, and (ii) literature-based approaches. The computational approach mainly uses statistical, machine learning, or soft-computing techniques [ 14,15] as discov- ery tools. On the other hand, a literature-based approach gathers relevant published information on genes and their interrelation-", + "addition, data from linkage or association studies (e.g. GWAS), or from high -throughput genetic screening experiments (e.g. CRISPR screening), or from animal gain -or-loss- of function studies, or from the gene -drug interactions, can also be exploited to predict potential gene functions. Integration of GeneBridge with data from these sources will further enhance the performance for gene function prediction, as is done in STRING [253], GeneMANIA [254] and Mitocarta [190, 255].", + "include the deep learning-driven pattern recognition models for analyzing the gene se- quences for identifying the possible future illness and developing mobile applications that can generalize the information from the genomic data. However, there is great demand for explainable Articial Intelligence models that are interpretable in decision-making. Author Contributions: The authors contributions are as follows, Conceptualization of the study,", + "Gene network inference algorithms are becoming accurate enough to be practically useful, at least when steady-state gene expression data are available, but efforts must be directedin assessing algorithm performances. In a few years, gene network inference will become as common as clustering for microarray data analysis. These algorithms will become moreTable IV Results of the application of network inference algorithms on the experiment data sets Data sets ARACNE BANJO NIR Clustering Random", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies" + ], + "answer": "Artificial intelligence (AI) has several potential applications in improving the annotation and interpretation of gene networks: 1. **Inference of Gene Functions**: AI techniques, such as network inference algorithms, can help infer the putative functions of unknown genes by linking them to genes with known functions that exhibit similar expression patterns. This approach can also prioritize candidate variants and predict disease inheritance modes to some extent [3]. 2. **Network Inference Techniques**: AI-driven network inference techniques can be utilized to infer biological processes and the potential phenotypic impact of variants in genes of unknown function. These techniques can provide powerful approaches to inferring phenotypic information where direct links to phenotype do not exist [4]. 3. **Computational Approaches**: AI, particularly through computational approaches using statistical, machine learning, or soft-computing techniques, serves as a discovery tool for finding gene networks. These approaches can complement literature-based methods that gather published information on genes and their interrelationships [6]. 4. **Pattern Recognition and Predictive Modeling**: Deep learning models, a subset of AI, can be used for pattern recognition in gene sequences to identify potential future illnesses. There is also a demand for explainable AI models that are interpretable in decision-making, which can enhance the understanding and application of genomic data [8]. These applications demonstrate how AI can significantly enhance the annotation and interpretation of gene networks by providing insights into gene functions, biological processes, and potential phenotypic impacts.", + "question": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_18 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_18 new file mode 100644 index 0000000..d22d56b --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_18 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2021 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "2010 - One Hundred Years of Pleiotropy A Retrospective.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2014 - Mendelian randomization genetic anchors for causal inference.pdf", + "2020 - Multivariate genomic scan implicates novel loci.pdf", + "2018 - The Use of Recombinant Inbred Strains in Systems Genetics and Functional Analyses in Behavioral Pharmacology.pdf", + "2022 - System Genetics in the Rat Family.pdf", + "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf" + ], + "extraction_id": [ + "2557b3fa-5aed-53f2-a4ca-afbed6154346", + "6b791cd6-0d92-52fb-ac76-d3b0bb4ed535", + "9b6ebb70-4cc0-5f53-bbbb-815ea191f2fa", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "3ac0a087-d982-5d06-b351-d2f1e635c5b0", + "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "3b23d583-7046-5dce-a506-fab0c2752977", + "38cbdb87-820c-587e-9511-69d0ba74457a", + "2e135c0b-af2c-54fa-8661-aa4a3e31c0da" + ], + "document_id": [ + "4198ec53-60f1-55d1-8759-b9ede1d098c0", + "9ab8b190-fb4f-5bb0-8d04-1cd07a42192a", + "c0995711-1389-52b7-a7a9-c92e5709fe43", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "05a32734-5dff-5430-b383-72a3d2e03792", + "8529f0c6-a65b-53ed-9663-02d52dd82631", + "337b2462-f1ec-530a-84de-97b13a0b9446", + "426b5aeb-1550-5039-8f2a-bd83d17c8648", + "bac2ab98-4317-59ed-99ef-deda8c22786d" + ], + "id": [ + "chatcmpl-AIGt6tExGqoQTRXd4fPWOb4MUvYWu", + "b3bb8c8a-a222-5b62-94c5-54910d338fa7", + "da910108-9a4b-5482-a4cb-bdb969cf959c", + "29d6e248-c012-56f7-85c5-1ee104731db0", + "c63cfaee-749e-547b-9c0a-086266f10670", + "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "6c9146cb-b00f-5f4c-8fc0-5a15a41405ec", + "89a8170c-a7b5-5236-8ef3-7d0e6918e584", + "12cdef3c-ff25-5349-8ef8-44f08065de4a", + "a62e58c3-d1a6-54e9-809f-d98488089738", + "6d34c5df-c9e5-5b22-b2af-2c1f191d984f" + ], + "contexts": [ + "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "advances, the more examples become known which canbe explained only under the assumption of pleiotropy (Plate 1910, quoted from M cKusick 1976, pp. 301302). His assertion of the extent and importance of pleiotropyhas been a central theme that has been challenged andstrengthened throughout the past 100 years as the way inwhich we study pleiotropy has changed. DEVELOPMENT OF PLEIOTROPIC RESEARCH One of the rst experimental studies of the mecha-", + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "34. Pyeritz, R.E. (1989) Pleiotropy revisited: molecular explanations of a classic concept. Am. J. Med. Genet. ,34, 124134. 35. Gruneberg, H. (1938) An analysis of the pleiotropic effects of a lethal mutation in the rat. Proc. R. Soc. Lond. B. ,125, 123144. 36. Wagner, G.P. and Zhang, J. (2011) The pleiotropic structure of the genotypephenotype map: the evolvability of complex organisms. Nat. Rev. Genet. ,12, 204213. 37. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.", + "21. Byars, S. G. et al. Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy. PLoS Genet. 13, e1006328 (2017). 22. Rodrguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation inuence human senescence and disease. Nat. Ecol. Evol. 1, 0055 (2017). 23. Institute for Health Metrics and Evaluation. Findings from the Global Burden of Disease Study 2017 (IHME, 2018).", + "traits can be due to shared molecular mechanisms and processes (true gene pleiotropy)or covariance can be due to statistical error or to linkage of neighboring, but mechanis-tically independent gene variants. This latter effect is particularly serious and is described in more length by Gerlai 4and in Wang5in the context of RI strains. GeneNetwork GeneNetwork (GN, www.genenetwork.org ) is an open web resource that enables", + "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676", + "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676" + ], + "answer": "The study of genetic pleiotropy has been advanced by data available on GeneNetwork.org through several key developments: 1. **Multi-Omics Analysis and Data from Multiple Species**: GeneNetwork.org has incorporated data from 10 different species and supports multi-omics analysis, which allows researchers to explore genetic pleiotropy across a wide range of organisms and biological data types. This comprehensive approach provides a richer understanding of how genes can influence multiple traits or diseases [4]. 2. **Systems Genetics Approach**: The platform enables a systems genetics approach, which contrasts with the traditional candidate gene approach. Instead of focusing on single gene mutations, it explores the relationships between diverse genetic and molecular markers and their resulting phenotypes and diseases. This approach is particularly useful for studying pleiotropy, as it considers the complex interactions and shared pathways that can lead to multiple phenotypic effects from a single genetic locus [5]. 3. **Open Web Resource**: GeneNetwork.org is an open web resource, making it accessible to a wide range of researchers. This accessibility facilitates collaborative research and data sharing, which are crucial for advancing the study of pleiotropy by allowing researchers to build on each other's findings and methodologies [8]. These features collectively enhance the ability to study genetic pleiotropy by providing comprehensive data, advanced analytical tools, and a collaborative platform for researchers.", + "question": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_19 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_19 new file mode 100644 index 0000000..2f45bff --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_19 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Multivariate genomic scan implicates novel loci.pdf", + "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "2020 - Mitonuclear genomics and aging.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2012 - Systems Biology in Aging Linking the Old and the Young.pdf" + ], + "extraction_id": [ + "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d", + "53c57cc4-4d43-505a-974c-442d06e144df", + "e26cef53-9a67-508e-8a29-2f40a6aa45b0", + "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "4109e561-4721-5f4e-b4d5-4353f8d1741d", + "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "df213743-7428-59be-ba19-2563f8ce5c70", + "a74345ec-ceee-5290-990b-ea338e735937" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "8529f0c6-a65b-53ed-9663-02d52dd82631", + "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8" + ], + "id": [ + "chatcmpl-AIGtEMdN8awavmFIcxxBrdyWkpsf8", + "496d27de-6dd0-5f6a-bedb-64d4c252981d", + "df726361-271a-5dbb-b6d1-03dab5a63006", + "300065ff-2ddb-532e-ab5d-a9b0903c8d21", + "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "15f6d690-61b1-5de3-ac40-10e46777afa8", + "9f662099-6f46-5af7-a6c1-4d0945b9a931", + "c96b67f8-ad31-50fd-b053-07b127938ef2", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "a05a46db-5443-566c-9494-212f86ee2eb3", + "016ee489-a313-5648-803d-db50217ae084" + ], + "contexts": [ + "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue et al . (2007) used GenAge to construct a modular network of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue et al", + "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", + "network analysis is a useful approach toward identifying genetic determinants of longevity . PLoS One , 2008 , 3(11), e3802. [38] Bell, R.; Hubbard, A.; Che ttier, R.; Chen, D.; Miller, J.P.; Kapahi, P.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A human protein interaction network shows conservation of aging processes between human and invertebrate species . PLoS Genet , 2009 , 5(3), e1000414. [39] Budovsky, A.; Abramovich, A.; Cohen, R.; Chalifa-Caspi, V.;", + "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "30. Vartiainen, S., Aarnio, V., Lakso, M. & Wong, G. Increased lifespan in transgenic Caenorhabditis elegans overexpressing human -synuclein. Exp. Gerontol. 41, 871 876 (2006). 31. Lpez-Otn, C. et al. The hallmarks of aging. Cell153, 1194 1217 (2013). 32. Kenyon, C. J. The genetics of ageing. Nature 464, 504 512 (2010). 33. Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417 425 (2015).", + "1118 compared to young ones. Overall, our results revealed that six pathways and six key genes might play pivotal roles in regulating longevity, and three interacting genes might be implicated in longevity. The results will not only provide new insight into the mechanisms of longevity, but also provide novel ideas for network-based approaches for longevity-related research. Keywords Drosophila melanogaster Longevity Gene Pathway Network Introduction", + "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "been associated with human longevity in genetic asso-ciation studies. The parallel emergence of networkapproaches offers prospects to develop multitargetdrugs and combinatorial therapies. Understandinghow the environment modulates aging-related genesmay lead to human applications and disease therapiesthrough diet, lifestyle, or pharmacological interven-tions. Unlocking the capacity to manipulate humanaging would result in unprecedented health benefits. I. Introduction", + "Network approaches are instrumental in discerning global properties of aging/lifespan regulators, making com- putational predictions and inferring the modularity and rela-tionships of various aging regulators. However, they should be applied with great caution as to avoid bias introduced by the literature, the lack of spatial and temporal information, or the limited coverage of the network [44]. 4. EPIGENETIC REGULATION OF AGING In addition to gene expression changes, the states of epi-" + ], + "answer": "GeneNetwork.org studies have identified novel genetic pathways related to aging and lifespan through various approaches. One notable method is the use of network identification by regression (NIR), which has been applied to identify novel pathways in the context of aging and lifespan [2]. Additionally, network-based approaches have revealed six pathways and six key genes that might play pivotal roles in regulating longevity, providing new insights into the mechanisms of longevity [6]. These findings highlight the potential of network-based methods to uncover novel genetic pathways associated with aging and lifespan.", + "question": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_2 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_2 new file mode 100644 index 0000000..ea55f4a --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_2 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "2001 - Demography in the age of genomics.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "9639b49f-d3be-5592-bb0e-a0341a7caa06", + "c362793d-c70f-5225-afe5-88098042daef", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "ad295276-e94f-53a4-9278-e6b93888ab10", + "8c423789-3641-5853-9cf3-f4a026ffb446", + "a043fdc7-8228-5f22-b6db-d3b1ad4eb6ae", + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "a52959f6-733a-58bb-93c0-65403fb72c83", + "efc8c74e-a862-5c6e-b947-96b664aaf0d0" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "ea43bb66-b6fe-5682-8f48-90568c080401", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "0f07fa43-feb6-5656-b7e7-b8faa86f5623" + ], + "id": [ + "chatcmpl-AIGrO4A8FLvBSq44CsQgHQD11jLhN", + "c63cfaee-749e-547b-9c0a-086266f10670", + "8999a4c7-e5de-539f-bee4-fd00cb69e7bb", + "e5320abf-d018-51c2-a386-aa822f605e1a", + "312eae52-ede7-5c13-8974-fce0126426cf", + "e71e1073-2800-5598-917f-00c3c08ed274", + "1c26e6f6-680b-5877-9600-fee25a42c943", + "8f299e3a-a7bc-5258-8f4d-0e964f89b35e", + "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "8744d4f9-5566-5435-98ce-2afae5f59ad3", + "82539c96-b4a2-50e2-bbdd-4458ff3f0d2a" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "analytical method, have been used to discover gene- environment interactions; some approaches address similar objectives, whilst others are complementary and can be ap- plied in sequence. Below we describe several of these ap- proaches, and refer the reader to another excellent review of gene-environment interaction methods [ 31]. (a)Established statistical approaches Until 2008, almost all studies of gene-environment interac- tions focused on testing hypotheses based on existing biolog-", + "ulated by non-genetic factors. Thus, the once esoteric topic of gene-environment interaction is now becoming mainstream and appealing to investigators across diversedisciplines; this has propelled major methodological in- novations for the discovery, replication, validation and translation of gene-environment interactions. The expo- nentiation of data resources for these purposes has demanded analytical solutions that address data dimen- sionality reduction. Although not yet extensively imple-", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "Eaves LJ 2006 Genotype x environment interaction in psychopathology: fact or artifact? Twin Res Hum Genet 9:18 Hunter DJ 2005 Geneenvironment interactions in human diseases. Nat Rev Genet 6:287298 Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG 2001 Replication validity of genetic association studies. Nat Genet 29:306309 Ioannidis JP, Gwinn M, Little J et al 2006 A road map for ef cient and reliable human genome epidemiology. Nat Genet 38:35", + "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "NU32CH13-Hu ARI 18 June 2012 13:45 effectively scan the entire genome for interac- tions with environment. Although innovative, the most effective study design and statistical approach for conducting gene-environment- wide interaction studies (GEWIS) remains unresolved (88). The greatest challenge for GEWIS involves nding a balance between rejecting true ndings resulting from stringent multiple-testing correction and reporting false-positive results. Several novel methods", + "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", + "13 132. Geneenvironment interaction: overcoming methodological challenges Rudolf Uher MRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, UK Abstract. While interacting biological effects of genes and environmental exposures (G E) form a natural part of the causal framework underlying disorders of human health, the detection of G E relies on inference from statistical interactions observed at popu-", + "A number of recent developments in twin methodology have taken place based on the incorporation of measured genotype information. Thisenables twin models to estimate how much of the genetic variation is dueto variation in a specific gene. Gene-environment interaction studies, link-Copyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html" + ], + "answer": "The latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org include the addition of data from 10 species, multi-omics analysis, updated code, and new tools, making it a valuable resource for predictive medicine and systems genetics [1]. These enhancements allow for more comprehensive analyses and facilitate the study of correlations between traits and data mining in genomic regions containing candidates for quantitative trait genes [4]. Additionally, GeneNetwork is continuously updated to support systems genetics analyses, which can aid in the discovery, replication, validation, and translation of gene-environment interactions [8].", + "question": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_20 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_20 new file mode 100644 index 0000000..c7171af --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_20 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "2021 - Genetics and genomics of arrhythmic.pdf", + "2020 - Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.pdf", + "2023 - Clinical, technical, and environmental biases.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "2018 - Genome-wide polygenic scores for common diseases.pdf", + "2022 - Coming of Age Human Genomics.pdf", + "2020 - Genome-wide assessment of genetic risk for systemic.pdf", + "2021 -Potter-Dickey- Genetic Susceptibility.pdf" + ], + "extraction_id": [ + "3c30b33b-8928-5cee-9c37-c70642fff75c", + "ada410d0-6b91-5959-b834-cc3389e29c5f", + "8292e291-87bb-5f04-8e40-fb2228da3927", + "50731787-cf17-5284-b3f4-2c551cb41c90", + "17c49e58-c89a-5495-b17f-adcade90a4c6", + "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "0a80e61e-648a-5122-9b17-8177bc734674", + "ca2e1560-db8f-5c3f-b7bf-dd1beaa94655", + "9b1cee76-2c59-50d6-a37c-8c593336fe33", + "567a2f7e-0ff9-5229-bfeb-066b6e6f50f6" + ], + "document_id": [ + "be0e50e0-3de8-53c5-8126-a0b618647f80", + "462ed035-e4fb-5847-a92d-927f05a2b58b", + "494779f3-1437-5b50-a9b2-3f616a048719", + "6a81e435-bd17-558d-850a-44ee3dbab5bd", + "4ece243f-acda-569d-b75d-37539260dcb3", + "4ece243f-acda-569d-b75d-37539260dcb3", + "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "45506895-eef1-57f4-8ca1-79fe23a2493f", + "af34f0df-a726-5cc4-844f-a5d67273d9a0", + "cb119609-daa3-56af-97ff-b809cc39c210" + ], + "id": [ + "chatcmpl-AIGtIvgudl04cUWtfjaShHQ8PZDZI", + "a374d88e-458e-5252-8b3a-5ca162fa6982", + "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "f36bf430-26bd-5031-a392-14f3c43367ab", + "4190e1d8-ae9e-5c42-8842-aa0a60a2bb2c", + "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "459f7eed-490a-5586-9d2a-20f721daa6bc", + "98da512f-fee2-501b-b093-9ee7ab22c5f9", + "d27fbbe8-aec0-510f-ab9d-1a0d4f0a1678", + "b3e446bb-e438-5d66-a34c-8e1de0ebb639" + ], + "contexts": [ + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "of genome-wide genotypes and publicly available data from large consortia, GRSs with a larger number of vari- ants are being used, and the predictive value of these genome-wide polygenic risk scores (PRSs) has substantially improved 50,51. PRSs can be derived using different approaches, however, these require both summary statistics from an exter -", + "use for estimation of polygenic risk scores (PRS) has grownin recent years. PRS screening may be used to determine therisk of common complex diseases for individuals and theiroffspring, and although it is not widely clinically availablenow, there is an ongoing interest in increasing its utility. Useof GWAS data from European populations for PRS esti-mation would subsequently impose a bias in favor of in- dividuals with similar ancestry, whereas limited bene ti s", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "Letters NATure GeNeTicsMethods Polygenic score derivation. Polygenic scores provide a quantitative metric of an individuals inherited risk based on the cumulative impact of many common polymorphisms. Weights are generally assigned to each genetic variant according to the strength of their association with disease risk (effect estimate). Individuals are scored based on how many risk alleles they have for each variant (for example, zero, one, or two copies) included in the polygenic score.", + "(Fig. 1B ). Polygenic risk scores (PRS) have emerged as promising biomarkers for the prediction of disease risk, not only in the area of cardiovascular disorders, but also oncology (21). These risk scores also have become increasingly available for a multitude of phenotypes and are systematically curated in a free online database (22). It has been shown that certain preexisting autoimmune diseases as well as the occurrence of imAE upon treatment are associated with", + "eases identify individuals with risk equivalent to monogenicmutations. Nat. Genet. ,50, 12191224. 13. Euesden, J., Lewis, C.M. and OReilly, P.F. (2015) PRSice: poly- genic risk score software. Bioinformatics ,31, 14661468. 14. Belsky, D.W., Moffitt, T.E., Sugden, K., Williams, B., Houts, R., McCarthy, J. and Caspi, A. (2013) Development and evalu- ation of a genetic risk score for obesity. Biodemography Soc. Biol.,59, 85100. 15. De Jager, P.L., Chibnik, L.B., Cui, J., Reischl, J., Lehr, S., Simon,", + "in tissue-specic regions or use gene co-expression information may provide a more comprehensive view of a specic gene or a gene networks role in modulating an individuals response to environmental variations, compared to that provided by the single candidate gene approach (Gamazon et al., 2015; Barth et al., 2020). Expression-based polygenic risk scores (ePRS) oer one such approach to understand the underlying genetic background linked to behavioral outcomes (Hari Dass" + ], + "answer": "Polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases by providing a quantitative metric of an individual's inherited risk based on the cumulative impact of many common polymorphisms [7]. These scores aggregate the genetic risk of individual alleles across the genome, which can significantly improve the prediction of future disease occurrence and aid in early diagnosis, intervention, and prevention strategies [5]. PRS can complement established clinical risk factors and intervention paradigms, thereby enhancing early diagnosis and prevention efforts for diseases such as type 2 diabetes (T2D) [6]. Additionally, PRS have emerged as promising biomarkers for predicting disease risk in various areas, including cardiovascular disorders and oncology [8]. By utilizing data from large consortia and genome-wide genotypes, the predictive value of these scores has substantially improved, allowing for a more comprehensive assessment of genetic risk [3].", + "question": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_3 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_3 new file mode 100644 index 0000000..55b5566 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_3 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2017 - Precise network modeling of systems genetics data using the Bayesian network webserver.pdf", + "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "2005 - How replicable are mRNA expression QTL.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "2019 - Systems genetics approaches to probe gene function.pdf", + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "c08af10b-f2ad-540b-be15-7cc101bf2dbc", + "046a82bb-8f86-5ecd-8879-34e569630a21", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "803030b1-07ab-5b8c-97cb-297339488484", + "ec624ebb-489a-5437-a721-f01cf981d0a7", + "0a4dc047-3b00-5657-b414-885d99b55d19", + "3276b251-2e60-53e8-8fd1-07702f486a43", + "8ef4c3cf-8018-5334-9f82-19c9e86739a5", + "858f630f-9443-5f13-ac40-8e16eadd9ba1" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "c80b6981-5243-55a2-b5d8-0d7ffb2f4505", + "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "699171c5-d983-50de-bcd2-fc3e117ff444", + "17264155-b665-59db-94cb-f4d67eac20fc", + "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "128224f1-3545-52c3-93cb-77c3cf4ec70a" + ], + "id": [ + "chatcmpl-AIGrUMBGxTc4nmy408W8WUAr2t9TQ", + "c63cfaee-749e-547b-9c0a-086266f10670", + "f53306e0-447d-5640-b26f-6b617ce35a46", + "da10a7f5-6d13-504c-8db9-d67a48a3193e", + "312eae52-ede7-5c13-8974-fce0126426cf", + "d500c4bd-50b1-5271-b7a6-42591225de7a", + "a9508122-3b14-5365-979c-ba580bdcb78f", + "a24d4dd1-29f8-596e-bc8b-f0dafaa82858", + "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "1e9adc57-45b4-5ac1-a0bf-a0b5ce07fef1", + "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", + "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "connect Genotype with Gene2 and Phenotype, knowledge of the Genotype still influences the predicted values of these variables. For example, Genotype = 1 may cause a decrease in Gene1 and this decrease in Gene1 will subsequently cause a reduction in Gene2. 4 Discussion Network modeling of biological datasets is often limited by the number of samples within a dataset, and the available data does not support the construction of precise and reliable large-scale networks", + "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", + "metadata (data about the data) are combined with sophisticated statistical and computation tools for the genetic dissection and synthesis of single traitsor entire systems of traits. One challenge facing investigators in the inter- pretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted many thousands of statistical tests with poorly understood", + "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "members o f pathway modules [78]. Other studies applied gene network modeling algorithms to identify the potential regulators in complex di seases, for example cardiomyopathy [79], hepatic steatosis [80], as well as coronary artery disease [81]. Finally, there are many other integrative approaches available for the analysis of multi -omics data, but have not yet been applied in mouse systems genetics studies. Examples include the transcriptome -wide", + "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine," + ], + "answer": "Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by allowing for the construction of biological networks that are predictive of molecular interactions and phenotypic outcomes. When data on DNA variation is combined with gene expression, protein interactions, and DNA-protein binding information, it enables the creation of comprehensive models that can predict complex phenotypes more accurately [6]. This integration facilitates the study of relationships between genes, pathways, and phenotypes, providing a more holistic view of the biological systems involved [2]. Additionally, the use of sophisticated statistical and computational tools in conjunction with multi-omics data allows for the genetic dissection and synthesis of traits, further enhancing predictive accuracy [7].", + "question": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_4 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_4 new file mode 100644 index 0000000..133e629 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_4 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2012 - Comparing Statistical Methods for Constructing Large Scale Gene Networks.pdf", + "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "2011 - Genetical genomics approaches for systems genetics.pdf", + "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "47c06e52-1923-58d0-9286-9674893a502a", + "5e93e58f-a415-5ead-9356-c749891269cc", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "2a75bfb9-6beb-54ef-b72b-25045ee3222d", + "29446d6f-fb32-5a6e-a51a-179c888091b2", + "29446d6f-fb32-5a6e-a51a-179c888091b2", + "3bdf080c-2715-5acc-bba4-717283851240", + "368bb4b5-bc26-5a39-95fc-561f58eb0e08", + "bee70000-17e9-5352-8c9c-349c78dfaa23" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "d9038328-bfea-5f73-87aa-6077b697e4db", + "5ded506d-7935-53f9-a118-57a9f3943376", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "ea0b9f5f-b1cf-5774-98aa-0f022c831fb8", + "1a20f715-5068-5c61-8396-59e6096fa7de", + "1a20f715-5068-5c61-8396-59e6096fa7de", + "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "17264155-b665-59db-94cb-f4d67eac20fc" + ], + "id": [ + "chatcmpl-AIGraUSt4UjtI0mL9sXfXnJsapOUk", + "c63cfaee-749e-547b-9c0a-086266f10670", + "aafbe14f-7ad3-5ad4-9951-90edecaceaa3", + "ac2029ae-498b-5ec0-ae10-f5729344cb5b", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "2e404112-d767-58f9-9bd3-f0220733759c", + "8bb5a6fb-9528-59cb-bc79-a1a52584abfa", + "59c4b4b6-6b08-5182-a493-e7f753b7eb87", + "9c01962f-fcac-57b3-a17d-487e37323230", + "1e19020c-c664-560b-8d2a-ef53ab8cb996", + "1755868d-9b84-5a6e-b6db-db70cb413656" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data Elissa J. Chesler1and Michael A. Langston2 1Life Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6124, USA 2Department of Computer Science, University of Tennessee, Knoxville, TN 379963450, USA Abstract: A series of genome-scale algorithms and high-performance implementations is described and shown to be useful in the genetic analysis of gene transcription. With", + "Combinatorial Genetic Regulatory Network Analysis Tools 163 In addition to expansive volumes of data, there is a growing complexity to the types of research questions that can be asked. We are presently developing approaches to compare graphs collected in a systems gene tic context to reect differences in time, tissue and treatment effects. Visualizatio n methods and compelling biological validation of novel results are essential to translate these methods and deliver them to the broader", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "larger networks well. Because of the computational complexity aswell as the memory requirements, these methods as currentlyimplemented are not the ideal choice for such large networks.WGCNA, GeneNet, ARACNE and SPACE, on the other hand,were designed to construct the gene network at very large scales.Also, it worth mentioning that the WGCNA package providesseveral useful tools to facilitate the analysis and visualization of resulting networks, including tools to identify subnetworks and an", + "Proc Natl Acad Sci U S A 100: 94409445. 32. Chesler E, Langston MA (2005) Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data. Proceedings,RECOMB Satellite Workshop on Systems Biology and Regulatory Genomics. 17 p.33. Abu-Khzam F, Langston M, Shanbhag P, Symons C (2006) Scalable Parallel Algorithms for FPT Problems. Algorithmica 45. 34. Langston M, Perkins A, Saxton A, Scharff J, Voy B (2006) Innovative", + "computational methods for transcriptomic data analysis. SAC 06: Proceedings of the 2006 ACM symposium on Applied computing. 35. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems 1695. 36. Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305311. 37. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant", + "plenary lecture, with a focus on the computational challengesin analyzing large datasets. The type of datasets discussed by Williams included the microarray type outputs first suggestedby Jansen and Nap ( 2001 ) for inclusion in genetical genomics analyses and are now extended to cross-platform datasets (Damerval et al. 1994; Ciobanu et al. 2010 ). A framework for carrying out the genetic analyses was described as being available through the GeneNetwork and WebQTL software", + "32. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kru glyak L, Bumgarner RE, Schadt EE: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks . Nat Genet 2008, 40 (7):854-861. 33. Vera G, Jansen RC, Suppi RL: R/parallel--speeding up bioinformatics analysis with R . BMC bioinformatics 2008, 9:390. 34. Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC: A statistical multiprobe model for analyzing cis and trans genes in genetical", + "Processing Large-Scale, High-Dimension Genetic and Gene Expression Data Cliona Molony, Solveig K. Sieberts, and Eric E. Schadt Abstract The now routine generation of large-scale, high-throughput data in mul- tiple dimensions (genotype, gene expression, and so on) provides a signicant challenge to researchers who desire to integrate data across these dimensions in" + ], + "answer": "Analyzing large-scale transcriptomic data within GeneNetwork.org presents several computational challenges and solutions: 1. **Computational Complexity and Memory Requirements**: The analysis of large networks is computationally complex and requires significant memory resources. Traditional methods may not be ideal for handling such large datasets [5]. 2. **Scalable Algorithms**: To address these challenges, scalable parallel algorithms have been developed. These algorithms are designed to handle fixed-parameter tractable (FPT) problems, which are crucial for managing large-scale data efficiently [6]. 3. **High-Performance Implementations**: Genome-scale algorithms and high-performance implementations are utilized to facilitate the genetic analysis of gene transcription, making it possible to manage and analyze large volumes of data effectively [2]. 4. **Visualization and Biological Validation**: Visualization methods and biological validation are essential to translate complex data into understandable results. These methods help in comparing graphs collected in a systems genetic context, reflecting differences in time, tissue, and treatment effects [3]. 5. **Tools for Large-Scale Network Construction**: Tools like WGCNA, GeneNet, ARACNE, and SPACE are specifically designed to construct gene networks at very large scales. These tools also provide functionalities for analyzing and visualizing resulting networks, including identifying subnetworks [5]. These solutions collectively help in overcoming the computational challenges associated with large-scale transcriptomic data analysis within GeneNetwork.org.", + "question": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_5 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_5 new file mode 100644 index 0000000..83c48c5 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_5 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "2016 - Genetic networks in mouse retinal ganglion cells.pdf", + "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "2021 - Lessons learned from the eMERGE Network balancing genomics.pdf", + "2012 - Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.pdf" + ], + "extraction_id": [ + "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "194c0d73-a9b7-5b5e-ac92-7dd689da6fc0", + "b881d0e1-11d4-578d-8560-0106c77d7a23", + "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "46616368-74e6-5605-9e43-9789e8e1bea1", + "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "8aecb357-2d62-51f9-9256-6fdf8c73791e", + "bc862e34-d30b-5882-9cc9-69f2bce72239" + ], + "document_id": [ + "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "ca0d3a29-7814-5d09-ad9d-e4143e87900d", + "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "d11a87ca-4989-59af-95e3-ab90af7d9212", + "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "cd0002dd-dcf1-567a-bf41-61eb0d6d982b", + "879c61e9-2efa-550b-b7ca-f88d67eb2199" + ], + "id": [ + "chatcmpl-AIGrg63GEuWBoLBB21tTvYo1XKFpy", + "c63cfaee-749e-547b-9c0a-086266f10670", + "c2225b34-e4a6-5147-998d-c2a5132d7a08", + "dc8fdfb1-539c-5941-bd4d-b595164cce9b", + "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "33dc52df-73a5-514e-8edb-33ae5046b8af", + "312eae52-ede7-5c13-8974-fce0126426cf", + "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "e17f1d54-7ea8-5a44-95b7-5d07f348574c", + "d519a13a-b6a0-505d-9a90-dd8f974721b4" + ], + "contexts": [ + "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", + "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", + "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", + "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", + "limit the applicability of genetic ndings in more diversepopulations. In the next phase of the network, the goalis to increase the diversity of underrepresented popula-tions, with targeted recruitment aimed at over 50% non-European ancestry. The lessons from enrollment andRoRs to diverse populations, even limited, will inform our next phase as we continue to strive for a more represen-", + "data available across all contributing consortia will facilitate systematic exploration of these correlated phenotypes with more sophisticated statistical methods for joint analysis5254, yielding greater insight into the underlying pathways and genetic networks they represent. As data from human genetic networks accrue, we will be better placed to test whether there is support for the notion of hub genesthat is, genes highly connected with others in the network, proposed by experi" + ], + "answer": "The inclusion of data from diverse populations on GeneNetwork.org aims to enhance the generalizability of findings by addressing the limitations associated with genetic studies that predominantly focus on less diverse populations. The goal is to increase the diversity of underrepresented populations, with targeted recruitment aimed at over 50% non-European ancestry [9]. This effort is expected to improve the applicability of genetic findings across different populations, thereby enhancing the generalizability of the research outcomes. By incorporating data from a broader range of genetic backgrounds, GeneNetwork.org can provide more comprehensive insights into genetic networks and pathways, which are applicable to a wider array of populations.", + "question": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_6 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_6 new file mode 100644 index 0000000..ff8f14e --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_6 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "2015 - Somatic mutation in cancer.pdf", + "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "2019 - Beyond Genome-wide Significance Integrative Approaches to the Interpretation and Extension of GWAS Findings for Alcohol Use Disorder.pdf", + "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "2017 - Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-response Behaviors in Model Organisms.pdf", + "2014 - Analyzing_gene_expression_data_in_mice_w.pdf", + "2022 -Restrepo- Predict impulsivity in children.pdf", + "2022 - Corticolimbic DCC gene co-expression networks as predictors of impulsivity in children.pdf" + ], + "extraction_id": [ + "0749dafa-17cf-5434-aad9-151a128e357b", + "feb6add1-ae89-5c82-8d59-6d4d66ea6779", + "300d8f31-5e42-5c17-a801-2f7afad3995e", + "82c75078-0fc5-508c-95ba-f2975fdec2c5", + "f623501d-c824-5334-98d7-dd599d0c063d", + "b3e6daa0-872e-546c-bee5-873b8f716c77", + "4c500aa5-faeb-5273-83a9-c5c91a27c697", + "848a85f6-382c-54e8-947b-670d71bb0639", + "10e3b0c3-e7cc-52e9-a6c2-e721a848bae5", + "8c7a2723-caa8-5ae1-a47c-c0c889443919" + ], + "document_id": [ + "38896019-c47e-5288-88a9-302779568cd3", + "0801355e-6f92-5526-a0b7-85a2bc859c51", + "38896019-c47e-5288-88a9-302779568cd3", + "24caaa62-2368-534f-8c42-f088c3409510", + "f59b3e10-a887-5708-b520-c5e8adb48dcd", + "38896019-c47e-5288-88a9-302779568cd3", + "045eff7e-5ff3-5b0e-9858-76eb8560e9d4", + "643f0642-d9c6-52f8-8b86-e469e778c003", + "15c3ab55-d6e6-532e-a655-759059ab7c07", + "fdecd4db-5e3a-5a3a-8145-28d05392822e" + ], + "id": [ + "chatcmpl-AIGrl5sKA3HUkZ2rgn7crnu6ec7EE", + "2aaaf2f2-8ea8-5f34-82ce-60cdce021b1c", + "06a4a00d-2b22-557a-b744-e4ac1fa8a5a2", + "cf9ea924-eb96-5444-9a8b-ed45c932b130", + "88756a11-58d2-59ec-8eed-08a96fc24ca0", + "f771b6cd-babd-56c2-a536-fbafc07c9be7", + "fd183495-c22b-5b6e-af12-ec216a838141", + "224463d2-e8a3-5a17-ab9b-9d6a39a081b8", + "18de97fd-e46c-5600-b45d-82de340e0d6b", + "366961c5-4349-5d93-abf5-203de53a4928", + "d7155850-29e4-5fec-b5a2-974f8ead2fef" + ], + "contexts": [ + "Lotan et al. Neuroinformatics of major neuropsychiatric disorders We demonstrated that although these disorders share a rela- tively small set of genes, there are two fundamental yet distinctgenetic components, or vectors, that are both shared by all sixdisorders. While the rst component is involved in CNS develop- ment, neural projections and synaptic transmission, the second", + "genetic variation) for any psychiatric disorder (Fig. 1), there is sufficient information to drawsome general conclusions. The polygenicity of psychiatric illness In addition to finding specific genes, molecu- lar genetics can provide information about theheritability of psychiatric disease, an approach that has led to some important insights about the genetic architecture of psychiatric illness.The degree of SNP sharing among disease cases estimates the common, inherited portion of a", + "of shared and unique genetic factors highlights key gene sets and molecular processesthat may ultimately translate into improved diagnosis and treatment of these debilitating disorders. Keywords: major neuropsychiatric disorders, neuroinformatics, cross-species, translational, genetic components, genome wide association studies, enrichment INTRODUCTION Common psychiatric disorders including attention-", + "6. D. H. Geschwind, J. Flint, Genetics and genomics of psychiatric disease. Science 349, 1489 1494 (2015). doi: 10.1126/science. aaa8954 ; pmid: 26404826 7. S. Cichon et al ., Genomewide association studies: History, rationale, and prospects for psychiatric disorders. Am. J. Psychiatry 166, 540 556 (2009). doi: 10.1176/ appi.ajp.2008.08091354 ; pmid: 19339359 8. A. Battle et al., Genetic effects on gene expression across human tissues. Nature 550, 204 213 (2017). doi: 10.1038/ nature24277 ; pmid: 29022597", + "the Psychiatric Genomics Consortium found that the results were highly correlated between methods in a comparison of methods applied across several psychiatric disorders ( Network Pathway Analysis Subgroup of Psychiatric Genomics Consortium 2015 ). A second limitation of pathway-based analysis is that it is still biased by our incomplete prior knowledge of gene function in the etiology of psychiatric illness. Despite these challenges, pathway-based analyses have identified biological pathways", + "Lotan et al. Neuroinformatics of major neuropsychiatric disorders GENES FROM THE NHGRI-CROSS-DISORDER SET CLUSTER IN THREE CO-EXPRESSION MODULES WITH DISTINCT SPATIO-TEMPORALEXPRESSION PATTERNS AND FUNCTIONAL BIASES One of the major properties of genes involved in regulation of", + "Genet. 2009; 85:847861. [PubMed: 19931040] Brownlee DJ, Fairweather I. Exploring the neurotransmitter labyrinth in nematodes. Trends Neurosci. 1999; 22:1624. [PubMed: 10088995] Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI Jr, Reich T, Schmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994; 55:149158. [PubMed: 8189735]", + "with shared effects on ve major psychiatric disorders: a genome- wide analysis. Lancet 381(9875):13711379 Davis S, Meltzer P (2007) Geoquery: a bridge between the gene expression omnibus (geo) and bioconductor. Bioinformatics 14:18461847 de Mooij-van Malsen AJG, Vinkers CH, Peterse DP, Olivier B, Kas MJH (2011) Cross-species behavioural genetics: a starting point for unraveling the neurobiology of human psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatr 35(6):13831390", + "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-", + "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-" + ], + "answer": "The insights obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders include the identification of two fundamental yet distinct genetic components shared by major neuropsychiatric disorders. The first component is involved in central nervous system (CNS) development, neural projections, and synaptic transmission [1]. Additionally, the polygenicity of psychiatric illnesses has been highlighted, indicating that psychiatric disorders are influenced by multiple genes, and there is a degree of single nucleotide polymorphism (SNP) sharing among disease cases, which helps estimate the common, inherited portion of these disorders [2]. Furthermore, shared and unique genetic factors have been identified, which highlight key gene sets and molecular processes that may lead to improved diagnosis and treatment of psychiatric disorders [3].", + "question": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_7 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_7 new file mode 100644 index 0000000..9f3f073 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_7 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2018 - Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics.pdf", + "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "2008 - Gene Expression Profiling.pdf", + "2005 - Part I Previous Research Track Record.pdf", + "2009 - Neuroscience in the era of functional genomics and systems biology.pdf", + "2022 -Madadi- AI RNA.pdf", + "2019 - Remodeling of epigenome and transcriptome.pdf", + "2018 - A survey on machine learning approaches in gene expression classification in modelling computational diagnostic system for complex diseases.pdf", + "2005 -Pomp- GenomeExploitation.pdf", + "2006 - Marker Assisted Backcrossing .pdf" + ], + "extraction_id": [ + "79e0c3a8-7d1b-5372-a776-7e9a76d09691", + "3bdf080c-2715-5acc-bba4-717283851240", + "00906abf-f4ca-53f2-a2b6-20359686e9ec", + "0853c5ab-3d98-565c-ba1f-50e5bd91d14c", + "52f30738-038c-58b4-af90-3e1c8735e729", + "ebd9b396-f870-5c65-9460-7f3da6c11e6c", + "4e757e70-c73b-59b2-8129-d253c4620f49", + "c7cd8df0-306c-5b1d-97b8-42410f4b82ed", + "d813f94e-cbde-502a-b387-a5cfd585ecca", + "99f23be3-af56-5ae5-9577-ae940bfd9653" + ], + "document_id": [ + "89534971-8c50-51ee-b2c4-35957579f911", + "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "1875d68b-adeb-5f91-8a67-91d881906238", + "08e29201-f2cc-5fd5-9c28-bc4b8aaaa936", + "03b9b993-8dd5-5b0d-9493-99fb9a624948", + "87ffccee-fc33-5373-948d-67736aa0f069", + "8355d7b5-9da9-5bb8-8a3e-6f77c667599c", + "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "5efc1bdf-f847-5eaf-a808-9cf71b9399ce" + ], + "id": [ + "chatcmpl-AIGrrCJF0xy80I2fCpFw4lJ55PYWM", + "5a61091b-7128-5326-a08c-9e53506eb0f4", + "1de27ae0-e471-5f99-baeb-6d53071de37b", + "92e845b4-fbdf-52e8-8ebd-39392ccdfeb7", + "d192b3fd-5ece-570a-a905-f94eef684af2", + "16baa529-fa53-5760-96b2-38779cab00e0", + "38245be7-bd5c-5711-94ba-794c16247aa9", + "14ac602a-df31-53c4-95cf-6ff078ddec34", + "c810e291-415f-5bee-a54b-1548ff0bacd5", + "5057d65b-2c37-5344-b757-3af91d22c690", + "8a074429-2464-5b19-8eb8-6775d588b24f" + ], + "contexts": [ + "The method takes as input a large cohort of individuals, wherethe input for each individual includes: (1) genotyping; (2) bulk ex-pression of genes in a certain tissue; (3) the relative abundance(proportions) of the various cell types in the tissue (it is possible to use computational deconvolution methods to predict cell-type proportions from bulk genomics data ( Newman et al. 2015 )). In", + "Filtering out the latter class of technical difficulty im-proved the recovery of genuine cis-modulated transcripts and thus to identify genes that are relevant to further down-stream regulation of gene expression and more complex phe-notypes (Ciobanu et al. 2010 ). Williams also discussed the power of a structured mapping population in model organisms and presented the Complex4 Funct Integr Genomics (2012) 12:1 9", + "genomic hybridization microarrays (8), can complement RNA expression data and result in novel discoveries. With the evolution and maturation of proteom ics, certainly combining serum- or tissue-based patterns of protein expression with RNA expression holds promise. Finally, other rich sources of complex data such as the literature can be used to complement our analysis of microar ray data (39). These analyses face significant challenges with respect to gene", + "data. To model the functional dependence we shall explore machine learning methods16, such as decision tree methods to predict the co-expressed gene profiles. As part of this study and in (E) Future work, see below, we will investigate the benefit of using comparative genomics in helping to lo cate and characterise the regul atory elements and signals. D(d) Integration and Modelling to infer regulato ry systems co-varying with disease status", + "derived from complex tissue such as brain show a high level of correspondence24,25. Such structure can be used to inform a new level of neuroscientific investigation that is not possible using standard analysis of differential expression2225. For example, one of the first such studies23 showed that gene networks could be used to provide a unifying method of identifying transcriptional targets of human brain evolution in", + "profiling of a multicellular organism,\" Science, vol. 357, no. 6352, pp. 661 -667, 2017. [68] X. Guo, W. Li, and F. Iorio, \"Convolutional neural networks for steady flow approximation,\" in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 481 -490. [69] V. Ntranos, L. Yi, P. Melsted, and L. Pachter, \"A discriminative learning approach to differentia l expression analysis for single -cell RNA -seq,\" Nature Methods, vol. 16,", + "levels can influence the ability to call differential gene expression (Oshlack and Wakefield 2009), we also included, as a feature, the average expression level of the genes in the young samples. All machine-learning algorithms assigned genes to the correct transcriptional change with age 67% 81% of the time on average, significantly above that of a random classification (50%) (Fig. 3B,C; Supplemental Fig. S3B,C ;Supplemental Table S3A,B ). Models de-", + "DNA. Microarray technology is helpful in capturing biological genetic information to computer data. Computational techniques can be applied on those large set of genetic data of every individuals with or without disease, so that the genes that are responsible for the disease occurrence can be po inted out. Differentially Expressed Genes (DEG) are identified using many techniques. Machine Learning (ML) algorithms plays a significant role in identifying the distinction between normal", + "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that", + "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that" + ], + "answer": "Advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues by enabling the prediction of cell-type proportions from bulk genomics data. This computational deconvolution is crucial for understanding the relative abundance of various cell types within a tissue, which is a key step in analyzing gene expression data from complex tissues [1]. Additionally, machine learning methods, such as decision tree methods, are explored to model functional dependencies and predict co-expressed gene profiles, which can further aid in the deconvolution process by identifying regulatory elements and signals that vary with disease status [4]. These advancements allow for more accurate and insightful analysis of gene expression data, facilitating the identification of transcriptional changes and regulatory networks in complex tissues.", + "question": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_8 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_8 new file mode 100644 index 0000000..6d8ac23 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_8 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - Skeletal muscle enhancer interactions identify genes controlling whole-body metabolism.pdf", + "2007 - How to infer gene networks from expression profiles.pdf", + "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf", + "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "2009 - Next generation synthetic gene networks.pdf", + "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf", + "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf" + ], + "extraction_id": [ + "1a87b58e-d091-582c-b96d-adac454fdf9d", + "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "213169b2-a4b0-5d5c-a297-c9a5896652ad", + "4c2afa3b-cf31-58ba-8ae8-2bf609f25dbc", + "d2dd2002-c8f6-5e2e-a06a-a8a20268c637", + "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "38e443bd-610e-5a1d-9f32-082e808d016a", + "c9ae0334-a2f7-5063-81aa-f313c77e4b65", + "7f3f1b6c-9fcd-5e8e-a4e0-d53da591d706" + ], + "document_id": [ + "fa738c86-1026-50f5-aebb-285ec92b209c", + "5067a047-b97d-522a-9a7e-5372e3bbd102", + "24caaa62-2368-534f-8c42-f088c3409510", + "1526d201-2f4e-5e6c-b2c8-8c825e741401", + "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "0046a766-21c6-582a-b868-685a24920faf", + "0046a766-21c6-582a-b868-685a24920faf", + "0d620c5e-a9ae-5b19-851b-37e40292ab8d", + "4060609b-1464-55fa-93cd-fefaf2cac900", + "6acebf19-b80c-5352-8201-99d5634fcc80" + ], + "id": [ + "chatcmpl-AIGrwafXsxRn06hAraC16E8hpnzWh", + "54f0e8c3-0322-51a6-b129-5850d0586c84", + "b713e667-ba32-514b-8373-0aebd9702cfc", + "640aa5eb-9b93-541a-ba5a-c1179c157c95", + "b6a01191-0181-547f-b37c-139a841296e4", + "958ecf38-a371-5a53-920f-b28dddea3fe4", + "ec2195b2-3ecd-5a55-a085-db9bb844f818", + "dac1a702-ecf9-5fe8-bb31-ea3c13bc94d9", + "c9155893-bf1f-516c-b509-f6d2014d275e", + "55660a79-e4ed-5fc7-8232-aa1401bfd3e8", + "a85fbdc3-7bb7-5d61-9d14-e15cc49fc28a" + ], + "contexts": [ + "dynamic16,17, and several studies have proposed that impaired enhancer activation could be at the origin of disease1821. Besides interacting with nearby promoters, enhancers also engage in long-range interactions. Indeed, it is estimated that approximately 3540% of all promoter-enhancer interactions are intervened by at least one gene22, which makes exact enhancer-target prediction challenging. Long-range enhancers interactions can be identi ed by chromosome conformation capture methods23,24.", + "motifs found in its promoter (gene-to-sequence). We will referto the ensemble of these inuence interactions as genenetworks. The interaction between two genes in a gene network does not necessarily imply a physical interaction, but can also referto an indirect regulation via proteins, metabolites and ncRNA that have not been measured directly. Inuence interactions include physical interactions, if the two interacting partnersare a transcription factor, and its target, or two proteins in the", + "~90,000 enhancer-promoter interactions (fig.S36). As expected, ~75% of enhancer-promoterinteractions occurred within the same TAD, and genes with more enhancers tended to have high- er expression (Fig. 5B and fig. S36). We inte-grated the Hi-C data with QTLs; surprisingly, QTLs involving SNPs distal to eGenes but linked by Hi-C interactions showed significantly stron-ger associations (as indicated by the QTL Pvalue) than those with SNPs directly in the eGene pro- moter or exons (Fig. 5C and fig. S37).", + "histone-modifying proteins, and other factors to regulate polymerase-II activity. Such factors can bind in close prox- imity to promoters to influence gene expression. However, there is substantial evidence that additional genetic elements referred to as enhancers play major roles in determining cell- specific patterns of gene expression. 1517 Initially identified >30 years ago, enhancer elements can be located at various distances from promoters, typically between 1 and 50 kilo-", + "involved in the regulation of the target genes of both networks, but that the interaction partners through which this regulation is established differs for both target genes.", + "variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31,26012606 (2015). 13. Schug, J. et al. Promoter features related to tissue specicity as measured by Shannon entropy. Genome Biol. 6,R33 (2005).14. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter interactome in human cells. Proc. Natl Acad. Sci. USA 111, E2191E2199 (2014). 15. Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specic gene", + "regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 1792117926 (2013). 16. Quang, D. X., Erdos, M. R., Parker, S. C. J. & Collins, F. S. Motif signatures in stretch enhancers are enriched for disease-associated genetic variants. Epigenet. Chromatin 8,23 (2015). 17. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell153, 307319 (2013).", + "networks. In fact, several of the higher-order networks we describe below rely on having multiple reliable and interoperable transcriptional activators and repressors for proper functioning. Even so, these engineered transcription factors have not yet been fully characterized, and if they are to be used as building blocks for complex gene networks, then knowledge of their in vivo kinetics and", + "BMC Genomics 2008, 9:310 http://www.biomedcen tral.com/1471-2164/9/310 Page 10 of 17 (page number not for citation purposes)A gene regulatory network comprising the regulatory interactions of the significant genes and the significant and enriched TFs is shown in Figure 5. Obvious are the five hubs, the core regulatory circuit derived from [17]. Well-regulated candidates can be identified like Acly and Fabp4 . Target and regulator at the same time is Ipf1. Discussion", + "32. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, et al. Systematic dissection of regulatory motifs in 2,000 predicted human enhancers using a massively parallel reporter assay. Genome research. 2013:gr. 144899.112. 33. Rands CM, Meader S, Ponting CP, Lunter G. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS genetics. 2014;10(7):e1004 525." + ], + "answer": "Enhancer-promoter interactions play a significant role in the regulation of gene networks by influencing gene expression levels and patterns. These interactions are crucial for determining cell-specific gene expression, as enhancers can regulate genes over long distances and are involved in complex regulatory networks [4]. Approximately 90,000 enhancer-promoter interactions have been identified, with a majority occurring within the same topologically associating domains (TADs), which suggests a structured and hierarchical organization of these interactions within the genome [3]. Genes with more enhancers tend to have higher expression levels, indicating that enhancers contribute to the regulation of gene expression by interacting with promoters [3]. Additionally, enhancer-promoter interactions can involve long-range interactions, making the prediction of specific enhancer-target relationships challenging [1]. These interactions are part of the broader gene networks that include various regulatory elements and factors, highlighting their importance in the regulation of gene networks as uncovered through platforms like GeneNetwork.org.", + "question": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_9 b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_9 new file mode 100644 index 0000000..909195f --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/intermediate_files/gpt4o_de_gn_9 @@ -0,0 +1,65 @@ +{ + "titles": [ + "2020 - The Genomics of Auditory.pdf", + "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "2021 - Interpreting type 1 diabetes risk.pdf", + "2020 - Visualizing and interpreting cancer genomics.pdf", + "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "2021 - Moving from in vitro to in vivo CRISPR screens.pdf" + ], + "extraction_id": [ + "0c7a27ef-7a65-5b32-8129-b168a336018a", + "203710b7-3267-5ecf-9397-b5becdaeead1", + "607a959f-6f63-5f18-8935-b76d87aa4820", + "ffc72db8-67ea-508a-aba1-d2592bd00ea2", + "2e588b06-841f-50d7-b161-330199d5c4cf", + "9cd48835-a7bf-50aa-928f-adb817e229d4", + "786d21d6-5544-5357-8163-1a1a96f6a791", + "d26b98eb-66cc-5185-9061-cda1fe904ba6", + "f740892a-7817-58b0-bec4-8648086b2353", + "6078715b-9964-5922-8fc9-5f105d0001ca" + ], + "document_id": [ + "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "eaae9d37-9c40-5e1c-9de9-d5ebcce9eae3", + "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "b9194555-5fdb-549e-9edb-d108132a7dd1", + "afe53f5a-3962-520f-be55-9df5bfdaad70", + "31d137b9-90a3-5b5a-ba4f-7a4d5b2c61bc" + ], + "id": [ + "chatcmpl-AIGs1N0h1lzkHw7McrwTnV7iXLWUI", + "9172db35-cec2-5970-8e5d-d73357f13abe", + "2020244c-6b6e-5613-900e-d7e32f6c4d57", + "f4ae7779-bbfc-5a13-bcd2-2e6724011eb8", + "1bdc47f8-9b31-5f89-8381-2238c4aec987", + "6b16574f-b513-5361-a0a8-a19f86ef6316", + "5297cd77-3ccf-570e-9ff9-bdb778638793", + "a49d3e49-6005-5890-ba75-8e5d59df13e5", + "eafc949f-7238-5776-bfef-5ccd9f91787e", + "c93bf9e1-39bd-59a9-8dd1-1b67a0853b8c", + "6442bc7c-4e2e-553f-82c4-b2f09e01823e" + ], + "contexts": [ + "high-throughput sequencing (ATAC-seq) allows the characterization of accessible chromatin re- gions,whichcorrespondtoareasoftranscriptionactivity(149).Examiningthethree-dimensional organization of the genome can facilitate the association between regulatory elements and their target genes by dividing the genome into discrete functional blocks, commonly known as topologically associating domains (139). The Encyclopedia of DNA Elements (ENCODE) and", + "variants, it is still unclear how multiple independent variants influence gene networks through changes in chromatin states. The Assay for Transpose Accessible Chromatin (ATAC-seq) was recently developed to address the need for sensitive as- says requiring less starting material, which also has the ability to simultaneously profile open chromatin, transcription factor- binding footprints, as well as nucleosome positioning in a single assay [ 57]. Given the limited availability of primary", + "Data Fig.4a). To relate cell-type-resolved accessible chromatin to gene expression, we created a single-cell RNA sequencing (scRNA-seq) refer - ence map of peripheral blood and pancreas. We assigned cell-type identi - ties for 90,495 cells to 29 clusters, which identified similar cell types and proportions to snATACseq (Extended Data Fig.5ac). To characterize cis-regulatory programs, we aggregated reads from cells within each snATACseq cluster and identified accessible chroma -", + "DNA methylation and ATAC-seq data (Supplementary Fig. 3). Integration across gene- and coordinate-centric views helps users examine genomic events in different chromosome contexts. For example, Xenas Visual Spreadsheet can help elucidate whether a gene amplification is part of a chromosomal arm duplication or a focal amplification (Supplementary Fig. 6).", + "matin accessibility assay ATAC-seq has been applied to single cells and has been shown to capture a higher order chromatin structure resembling the profiles generated by Hi-C [ 72]. Additionally, for CAD candidate genes that are transcrip- tion factors (TF), such as TCF21 and STAT3, protein-DNA interactions could be studied on a genome-wide scale using chromatin immunoprecipitation sequencing (ChIP-Seq). Recently, ChIP-Seq performed against TCF21 in human cor-", + "seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Formaldehyde- Assisted Isolation of Regulatory Elements (FAIRE-seq) and DNase I hypersensitive sites sequencing (DNase-seq). The integration of DNA methylation data (WGBS) and chromatin accessibility data (ATAC-seq) with established ChIP-seq mark ers have provided an opportunity to create high-resolution", + "94. Mumbach MR, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919922. doi: 10.1038/nmeth.3999. 95. Kumasaka N, et al. Fine-mapping cellular QTLs with RASQUAL and ATAC- seq. Nat Genet. 2016;48:206213. doi: 10.1038/ng.3467. 96. Buenrostro JD, et al. ATAC-seq: a method for assaying chromatin acces- sibility genome-wide. Curr Protoc Mol Biol. 2015;109:21.29.121.29.9. doi: 10.1002/0471142727.mb2129s109.", + "CpG sites. Single nucleus Assay for Transposase-Accessible Chromatinusing sequencing (snATACseq) was informative of chromatin opennessin various kidney cell types. The RegulomeDB is a database with exten-sive epigenetic annotation for SNPs. The promoter capture HiC (PCHiC) sequencing data identified sequence interaction with gene promoters,", + "a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21.29.2121.29.29. https ://doi.org/10.1002/04711 42727 .mb212 9s109 Bysani M etal (2019) ATAC-seq reveals alterations in open chromatin in pancreatic islets from subjects with type 2 diabetes. Sci Rep 9:7785. https ://doi.org/10.1038/s4159 8-019-44076 -8 Camp JG etal (2015) Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl", + "genes are involved with multiple biological features. RNA sequencing has been coupled with protein quantication (DNA barcoded antibodies to quantify protein expression) and ATAC-seq to facilitate the study of genes involved with chromatin accessibility remodeling. their environment [14 , 31 , 88 , 95 , 105] . Advances in multiplexed gene editing and transcriptional programing will also enable CRISPR screens" + ], + "answer": "The integration of ATAC-seq data with RNA-seq data can provide valuable insights into chromatin accessibility and gene regulation by combining information about open chromatin regions with gene expression profiles. ATAC-seq is a technique that characterizes accessible chromatin regions, which are often associated with transcriptional activity [1]. This method can simultaneously profile open chromatin, transcription factor-binding footprints, and nucleosome positioning [2]. By integrating this data with RNA-seq, which measures gene expression levels, researchers can relate chromatin accessibility to gene expression patterns. For example, by creating a reference map using single-cell RNA sequencing (scRNA-seq) and assigning cell-type identities, researchers can relate cell-type-resolved accessible chromatin to gene expression [3]. This integration helps in identifying cis-regulatory programs by aggregating reads from cells within each ATAC-seq cluster and linking them to gene expression data. Overall, the integration of ATAC-seq and RNA-seq data allows for a comprehensive understanding of how chromatin accessibility influences gene regulation, providing insights into the regulatory elements that control gene expression in different cellular contexts.", + "question": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" +}
\ No newline at end of file |