From 00cba4b9a1e88891f1f96a1199320092c1962343 Mon Sep 17 00:00:00 2001 From: ShelbySolomonDarnell Date: Thu, 17 Oct 2024 12:24:26 +0300 Subject: Docker image built to run code, all evals run using R2R --- .../data/responses/gpt4o/de_gn_responses.json | 3222 ++++++++++++++++++++ 1 file changed, 3222 insertions(+) create mode 100644 gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json (limited to 'gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json') diff --git a/gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json b/gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json new file mode 100644 index 0000000..ed04cba --- /dev/null +++ b/gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json @@ -0,0 +1,3222 @@ +{ + "1": { + "results": { + "completion": { + "id": "chatcmpl-AIGrF12QPoEwoc2D22aSA5ivwYW2D", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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].\n\nMoreover, 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].\n\nOverall, 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.", + "role": "assistant" + } + } + ], + "created": 1728918069, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 217, + "prompt_tokens": 1160, + "total_tokens": 1377 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "score": 0.7168012598989943, + "metadata": { + "text": "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.", + "title": "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "c54da858-9620-588e-8e41-76a960af2ff6", + "extraction_id": "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "e26ebc1e-e05d-56fb-8718-604275994a84", + "score": 0.6923868243463851, + "metadata": { + "text": "Processing Large-Scale, High-Dimension Genetic 325\nanother. We anticipate these types of networks becoming increasingly important in\nthe 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\nthat cause disease. The integration of genotypic and expression and other data have\nrecently been shown, in a Bayesian network framework [76], to enhance the overall", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "b17b43c6-1ba8-5849-8664-3b5cd78877b5", + "score": 0.6905426957718185, + "metadata": { + "text": "2. GENETICAL GENOMICS\nIn recent years, there has been growing interest in uniting genetic and genomic\napproaches to enable more comprehensive dissections of complex traits and their\ngenetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "title": "2005 -Pomp- GenomeExploitation.pdf", + "version": "v0", + "chunk_order": 641, + "document_id": "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "extraction_id": "80f97b13-9dd9-5d52-9d55-0abac724605e", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "037c669c-da80-5e1e-abe3-c4344145a4ed", + "score": 0.6905426957718185, + "metadata": { + "text": "2. GENETICAL GENOMICS\nIn recent years, there has been growing interest in uniting genetic and genomic\napproaches to enable more comprehensive dissections of complex traits and their\ngenetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "title": "2006 - Marker Assisted Backcrossing .pdf", + "version": "v0", + "chunk_order": 641, + "document_id": "5efc1bdf-f847-5eaf-a808-9cf71b9399ce", + "extraction_id": "da78b007-359c-548c-8cb0-ba4a3dab0f86", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "fb5944f3-bb0e-599e-827c-a8b7c6934746", + "score": 0.6871884055621492, + "metadata": { + "text": "42.Chesler EJ, et al. 2005. Complex trait analysis of gene expression uncovers\npolygenic and pleiotropic networks that modulate nervous system func-tion. Nat. Genet. 37:233242.\n43.Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, develop-\ning a resource for mammalian systems genetics: a status report of theWellcome Trust cohort. Mamm. Genome 19:379 381.\n44.Xiao J, et al. 2010. A novel strategy for genetic dissection of complex traits:", + "title": "2013 - Host Genes and Resistance.pdf", + "version": "v0", + "chunk_order": 1268, + "document_id": "cc6cf2b5-0440-51e8-aad4-d0b4b5331ab2", + "extraction_id": "661e7fb0-804c-53e2-b948-6512c372ac57", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "a860695e-fe40-55eb-9eb8-072e1daf5cf2", + "score": 0.684656180780394, + "metadata": { + "text": "multiple-SNP analysis of GWAS summary statistics identiesadditional variants inuencing complex traits. Nat Genet\n44(369375):S1S3. doi: 10.1038/ng.2213\nYang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls\nin the application of mixed-model association methods. NatGenet 46:100106. doi: 10.1038/ng.2876\nYazbek SN, Buchner DA, Geisinger JM et al (2011) Deep congenic", + "title": "2014 - Fine-mapping QTLs in advanced intercross lines and other.pdf", + "version": "v0", + "chunk_order": 217, + "document_id": "eb30392e-f079-511d-8c6c-a6e6c98d2167", + "extraction_id": "a5c455c9-50f6-5f12-84cd-26d335001e6b", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "22301737-122c-57be-a2f1-9d631ad101b3", + "score": 0.675520160732255, + "metadata": { + "text": "10. \nThe 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\n11,12 and haplotype \nassociation studies using large panels of regular inbred strains of mice, and even humans, are", + "title": "2007 - Latexin is a newly discovered regulator of hematopoietic stem cells.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "63467ba8-940b-59f6-bbd5-0b0ce7883d49", + "extraction_id": "91470df2-7451-59d2-af9f-98cdf2f85486", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "101c1f27-4a98-5d1c-b013-c5f1950aee95", + "score": 0.6743107230292936, + "metadata": { + "text": "tive analysis of omics summary data reveals putative mechanisms \nunderlying complex traits. Nat Commun 9:918\n 33. Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: \na tool for genome-wide complex trait analysis. Am J Hum Genet \n88:7682\n 34. Zeisel A, Hochgerner H, Lnnerberg P, Johnsson A, Memic F, \nvan der Zwan J etal (2018) Molecular architecture of the mouse \nnervous system. Cell 174:999.e221014.e22\n 35. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ (2016) RVTESTS:", + "title": "2020 - Large?scale pathway specific polygenic risk and transcriptomic.pdf", + "version": "v0", + "chunk_order": 147, + "document_id": "96119357-a6dd-5ea3-8bcb-9c047f0a336e", + "extraction_id": "a53c7001-432d-5289-9cc1-b3d75a721da4", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "91ac7cb9-ec59-5bd6-9f24-aa840caf2c27", + "score": 0.6738772379379455, + "metadata": { + "text": "used to identify molecular traits involved in the p athology of diseases and to eluci- \ndate the networks underlying complex phenotypes. Re cent studies have pushed the \ngenetical genomics concept further towards data int egration and interpretation \nwithin and across molecular levels, and have also r evealed remaining challenges. \nThe focus of this review is to discuss these challe nges and their possible solutions in", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 618, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "4a7ed73c-26c7-5852-8a02-a39cd0d611ec", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "6e933f07-26d6-5cf1-8ee0-9bf6ec68b1ff", + "score": 0.6721933278807058, + "metadata": { + "text": "2 large populations. The new methods have allowed us to dissect the genetic architecture of \ncomplex disorders including the identification of the causal genomic loci, estimation of \nthe disease heritability, estimation of effect sizes of different loci and their non-additive \ninteractions. \nLinkage analysis \n \n The earlier breakthroughs in linking genotype with phenotype involved studies of \nMendelian disorders that can be mapped to a single gene and a single mutation. These", + "title": "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf", + "version": "v0", + "chunk_order": 56, + "document_id": "263d327b-f5db-54e4-a215-b3f8a51cd7d6", + "extraction_id": "cec82840-0f05-5fc5-bfcf-a4b928124fef", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIGrO4A8FLvBSq44CsQgHQD11jLhN", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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].", + "role": "assistant" + } + } + ], + "created": 1728918078, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 114, + "prompt_tokens": 1190, + "total_tokens": 1304 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7165116836719714, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "8999a4c7-e5de-539f-bee4-fd00cb69e7bb", + "score": 0.6932196799981797, + "metadata": { + "text": "analytical method, have been used to discover gene-\nenvironment interactions; some approaches address similar\nobjectives, whilst others are complementary and can be ap-\nplied in sequence. Below we describe several of these ap-\nproaches, and refer the reader to another excellent review of\ngene-environment interaction methods [ 31].\n(a)Established statistical approaches\nUntil 2008, almost all studies of gene-environment interac-\ntions focused on testing hypotheses based on existing biolog-", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "9639b49f-d3be-5592-bb0e-a0341a7caa06", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "e5320abf-d018-51c2-a386-aa822f605e1a", + "score": 0.6798188686370898, + "metadata": { + "text": "ulated by non-genetic factors. Thus, the once esoteric\ntopic of gene-environment interaction is now becoming\nmainstream and appealing to investigators across diversedisciplines; this has propelled major methodological in-\nnovations for the discovery, replication, validation and\ntranslation of gene-environment interactions. The expo-\nnentiation of data resources for these purposes has\ndemanded analytical solutions that address data dimen-\nsionality reduction. Although not yet extensively imple-", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "c362793d-c70f-5225-afe5-88098042daef", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6637628479286614, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "e71e1073-2800-5598-917f-00c3c08ed274", + "score": 0.6632876000828993, + "metadata": { + "text": "Eaves LJ 2006 Genotype x environment interaction in psychopathology: fact or artifact? Twin \nRes Hum Genet 9:18\nHunter DJ 2005 Geneenvironment interactions in human diseases. Nat Rev Genet \n6:287298\nIoannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG 2001 Replication validity \nof genetic association studies. Nat Genet 29:306309\nIoannidis JP, Gwinn M, Little J et al 2006 A road map for ef cient and reliable human genome \nepidemiology. Nat Genet 38:35", + "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "version": "v0", + "chunk_order": 205, + "document_id": "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "extraction_id": "ad295276-e94f-53a4-9278-e6b93888ab10", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "1c26e6f6-680b-5877-9600-fee25a42c943", + "score": 0.6603226864145597, + "metadata": { + "text": "GeneNetwork is an open-access database that collates genomic\ninformation of diverse experimental crosses and reference panels\nas well as phenotypic data from miscellaneous research groups\n[26].\nStatistics\nData generation, statistical analysis and graph creation were\nperformed with SPSS Statistics 21 (IBM, Ehningen, Germany). As\nappropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "title": "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "extraction_id": "8c423789-3641-5853-9cf3-f4a026ffb446", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "8f299e3a-a7bc-5258-8f4d-0e964f89b35e", + "score": 0.6585033739505199, + "metadata": { + "text": "NU32CH13-Hu ARI 18 June 2012 13:45\neffectively scan the entire genome for interac-\ntions with environment. Although innovative,\nthe most effective study design and statistical\napproach for conducting gene-environment-\nwide interaction studies (GEWIS) remains\nunresolved (88). The greatest challenge for\nGEWIS involves nding a balance between\nrejecting true ndings resulting from stringent\nmultiple-testing correction and reporting\nfalse-positive results. Several novel methods", + "title": "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 66, + "document_id": "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "extraction_id": "a043fdc7-8228-5f22-b6db-d3b1ad4eb6ae", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "score": 0.6570818628620003, + "metadata": { + "text": "1 \n GeneNetwork: a continuously updated tool for systems genetics analyses \n \nPamela M. Watson1, David G. Ashbrook1 \n \n1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science \nCenter, Memphis, TN 38163, USA \n \nAbstract \n \nGeneNetwork and its earlier iteration , WebQTL, have now been an important database and \ntoolkit for quantitative trait genetics research for two decades. Recent improvements to", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "8744d4f9-5566-5435-98ce-2afae5f59ad3", + "score": 0.6550541335279783, + "metadata": { + "text": "13 132. Geneenvironment interaction: \novercoming methodological challenges\nRudolf Uher\nMRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings \nCollege London, UK\nAbstract. While interacting biological effects of genes and environmental exposures (G \n E) form a natural part of the causal framework underlying disorders of human health, \nthe detection of G E relies on inference from statistical interactions observed at popu-", + "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "extraction_id": "a52959f6-733a-58bb-93c0-65403fb72c83", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "82539c96-b4a2-50e2-bbdd-4458ff3f0d2a", + "score": 0.6547671361336992, + "metadata": { + "text": "A number of recent developments in twin methodology have taken\nplace 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?\nhttp://www.nap.edu/catalog/9995.html", + "title": "2001 - Demography in the age of genomics.pdf", + "version": "v0", + "chunk_order": 436, + "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "extraction_id": "efc8c74e-a862-5c6e-b947-96b664aaf0d0", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIGrUMBGxTc4nmy408W8WUAr2t9TQ", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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].", + "role": "assistant" + } + } + ], + "created": 1728918084, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 142, + "prompt_tokens": 1160, + "total_tokens": 1302 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6984842419624329, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "f53306e0-447d-5640-b26f-6b617ce35a46", + "score": 0.6796948313713074, + "metadata": { + "text": "Conclusion \nGeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. \nHere we have used GeneNetwork to explore an inflammatory phenotype, and identified a small \nnumber of plausible candidate genes. A similar workflow can be used for any trait on \nGeneNetwork, or for any phenotype collected by an investigator in a genetically diverse \npopulation. GeneNetwork can allow users to study relationships between genes, pathways, and \nphenotypes in an easy to use format.", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "c08af10b-f2ad-540b-be15-7cc101bf2dbc", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "da10a7f5-6d13-504c-8db9-d67a48a3193e", + "score": 0.6796948313713074, + "metadata": { + "text": "Conclusion \nGeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. \nHere we have used GeneNetwork to explore an inflammatory phenotype, and identified a small \nnumber of plausible candidate genes. A similar workflow can be used for any trait on \nGeneNetwork, or for any phenotype collected by an investigator in a genetically diverse \npopulation. GeneNetwork can allow users to study relationships between genes, pathways, and \nphenotypes in an easy to use format.", + "title": "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "extraction_id": "046a82bb-8f86-5ecd-8879-34e569630a21", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6671545703915029, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "d500c4bd-50b1-5271-b7a6-42591225de7a", + "score": 0.6594731408200607, + "metadata": { + "text": "connect Genotype with Gene2 and Phenotype, knowledge of the \nGenotype still influences the predicted values of these variables. \nFor example, Genotype = 1 may cause a decrease in Gene1 and this \ndecrease in Gene1 will subsequently cause a reduction in Gene2.\n4 Discussion\nNetwork modeling of biological datasets is often limited by the \nnumber of samples within a dataset, and the available data does not \nsupport the construction of precise and reliable large-scale networks", + "title": "2017 - Precise network modeling of systems genetics data using the Bayesian network webserver.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "c80b6981-5243-55a2-b5d8-0d7ffb2f4505", + "extraction_id": "803030b1-07ab-5b8c-97cb-297339488484", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6555604338645935, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "a24d4dd1-29f8-596e-bc8b-f0dafaa82858", + "score": 0.6548624634742737, + "metadata": { + "text": "metadata (data about the data) are combined with\nsophisticated statistical and computation tools for\nthe genetic dissection and synthesis of single traitsor entire systems of traits.\nOne challenge facing investigators in the inter-\npretation 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\nthousands of statistical tests with poorly understood", + "title": "2005 - How replicable are mRNA expression QTL.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "699171c5-d983-50de-bcd2-fc3e117ff444", + "extraction_id": "0a4dc047-3b00-5657-b414-885d99b55d19", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "score": 0.6539399027824402, + "metadata": { + "text": "accuracy of predictive networks [40, 5153]. We have also recently demonstrated\nhow this class of network can be used to inform associations identied in GW Astudies [40].\n9 Summary\nThe 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", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "1e9adc57-45b4-5ac1-a0bf-a0b5ce07fef1", + "score": 0.6534584760665894, + "metadata": { + "text": "members o f pathway modules [78]. Other studies applied gene network modeling algorithms to identify the \npotential regulators in complex di seases, for example cardiomyopathy [79], hepatic steatosis [80], as well as \ncoronary artery disease [81]. \n Finally, there are many other integrative approaches available for the analysis of multi -omics data, but \nhave not yet been applied in mouse systems genetics studies. Examples include the transcriptome -wide", + "title": "2019 - Systems genetics approaches to probe gene function.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "extraction_id": "8ef4c3cf-8018-5334-9f82-19c9e86739a5", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "score": 0.6517401156735328, + "metadata": { + "text": "gathered together into an easily accessible format, not siloed into disparate data pools that \ncannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal \nmodels of so called precision medicine, although perhaps more accurately, we want \npredictive medicine , where a phenotypic outcome (such as disease) can be predicted , and \navoided . \nGeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIGraUSt4UjtI0mL9sXfXnJsapOUk", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Analyzing large-scale transcriptomic data within GeneNetwork.org presents several computational challenges and solutions:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nThese solutions collectively help in overcoming the computational challenges associated with large-scale transcriptomic data analysis within GeneNetwork.org.", + "role": "assistant" + } + } + ], + "created": 1728918090, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 281, + "prompt_tokens": 1295, + "total_tokens": 1576 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.700504800692344, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "aafbe14f-7ad3-5ad4-9951-90edecaceaa3", + "score": 0.6961420981926354, + "metadata": { + "text": "Combinatorial Genetic Regulatory Network Analysis\nTools for High Throughput Transcriptomic Data\nElissa J. Chesler1and Michael A. Langston2\n1Life Sciences Division, Oak Ridge National Laboratory,\nP.O. Box 2008, Oak Ridge, TN 37831-6124, USA\n2Department of Computer Science, University of Tennessee,\nKnoxville, TN 379963450, USA\nAbstract: A series of genome-scale algorithms and high-performance implementations\nis described and shown to be useful in the genetic analysis of gene transcription. With", + "title": "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "d9038328-bfea-5f73-87aa-6077b697e4db", + "extraction_id": "47c06e52-1923-58d0-9286-9674893a502a", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "ac2029ae-498b-5ec0-ae10-f5729344cb5b", + "score": 0.6620665389915272, + "metadata": { + "text": "Combinatorial Genetic Regulatory Network Analysis Tools 163\nIn addition to expansive volumes of data, there is a growing complexity to the types\nof research questions that can be asked. We are presently developing approaches to\ncompare graphs collected in a systems gene tic context to reect differences in time,\ntissue and treatment effects. Visualizatio n methods and compelling biological validation\nof novel results are essential to translate these methods and deliver them to the broader", + "title": "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "version": "v0", + "chunk_order": 1029, + "document_id": "5ded506d-7935-53f9-a118-57a9f3943376", + "extraction_id": "5e93e58f-a415-5ead-9356-c749891269cc", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6521361068527733, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "2e404112-d767-58f9-9bd3-f0220733759c", + "score": 0.644407372343323, + "metadata": { + "text": "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\nresulting networks, including tools to identify subnetworks and an", + "title": "2012 - Comparing Statistical Methods for Constructing Large Scale Gene Networks.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "ea0b9f5f-b1cf-5774-98aa-0f022c831fb8", + "extraction_id": "2a75bfb9-6beb-54ef-b72b-25045ee3222d", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "8bb5a6fb-9528-59cb-bc79-a1a52584abfa", + "score": 0.64085860970195, + "metadata": { + "text": "Proc Natl Acad Sci U S A 100: 94409445.\n32. Chesler E, Langston MA (2005) Combinatorial Genetic Regulatory Network\nAnalysis Tools for High Throughput Transcriptomic Data. Proceedings,RECOMB Satellite Workshop on Systems Biology and Regulatory Genomics.\n17 p.33. Abu-Khzam F, Langston M, Shanbhag P, Symons C (2006) Scalable Parallel\nAlgorithms for FPT Problems. Algorithmica 45.\n34. Langston M, Perkins A, Saxton A, Scharff J, Voy B (2006) Innovative", + "title": "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "version": "v0", + "chunk_order": 196, + "document_id": "1a20f715-5068-5c61-8396-59e6096fa7de", + "extraction_id": "29446d6f-fb32-5a6e-a51a-179c888091b2", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "59c4b4b6-6b08-5182-a493-e7f753b7eb87", + "score": 0.6403773832208723, + "metadata": { + "text": "computational methods for transcriptomic data analysis. SAC 06: Proceedings\nof the 2006 ACM symposium on Applied computing.\n35. Csardi G, Nepusz T (2006) The igraph software package for complex network\nresearch. InterJournal Complex Systems 1695.\n36. Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list\nenrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305311.\n37. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant", + "title": "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "version": "v0", + "chunk_order": 197, + "document_id": "1a20f715-5068-5c61-8396-59e6096fa7de", + "extraction_id": "29446d6f-fb32-5a6e-a51a-179c888091b2", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "9c01962f-fcac-57b3-a17d-487e37323230", + "score": 0.640213031775148, + "metadata": { + "text": "plenary lecture, with a focus on the computational challengesin analyzing large datasets. The type of datasets discussed by\nWilliams included the microarray type outputs first suggestedby Jansen and Nap ( 2001 ) for inclusion in genetical genomics\nanalyses and are now extended to cross-platform datasets\n(Damerval et al. 1994;\n Ciobanu et al. 2010 ). A framework\nfor carrying out the genetic analyses was described as being\navailable through the GeneNetwork and WebQTL software", + "title": "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "extraction_id": "3bdf080c-2715-5acc-bba4-717283851240", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "1e19020c-c664-560b-8d2a-ef53ab8cb996", + "score": 0.6394572448568718, + "metadata": { + "text": "32. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kru glyak L, Bumgarner RE, \nSchadt EE: Integrating large-scale functional genomic data to dissect the \ncomplexity of yeast regulatory networks . Nat Genet 2008, 40 (7):854-861. \n33. Vera G, Jansen RC, Suppi RL: R/parallel--speeding up bioinformatics \nanalysis with R . BMC bioinformatics 2008, 9:390. \n34. Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC: A statistical \nmultiprobe model for analyzing cis and trans genes in genetical", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 311, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "368bb4b5-bc26-5a39-95fc-561f58eb0e08", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "1755868d-9b84-5a6e-b6db-db70cb413656", + "score": 0.6389806841892619, + "metadata": { + "text": "Processing Large-Scale, High-Dimension\nGenetic and Gene Expression Data\nCliona Molony, Solveig K. Sieberts, and Eric E. Schadt\nAbstract The now routine generation of large-scale, high-throughput data in mul-\ntiple dimensions (genotype, gene expression, and so on) provides a signicant\nchallenge to researchers who desire to integrate data across these dimensions in", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "bee70000-17e9-5352-8c9c-349c78dfaa23", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIGrg63GEuWBoLBB21tTvYo1XKFpy", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.", + "role": "assistant" + } + } + ], + "created": 1728918096, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 124, + "prompt_tokens": 1112, + "total_tokens": 1236 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6721890568733215, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "c2225b34-e4a6-5147-998d-c2a5132d7a08", + "score": 0.6535828709602356, + "metadata": { + "text": "GeneNetwork provided the platform for correlation analysis,\nprincipal component generation, and linkage analysis. In\ngeneral, datasets were queried for gene symbols, downloaded\nfrom GeneNetwork, and additional analysis was performed\nin R whenever necessary. P-values mentioned in relation to\nPearsons coecient throughout this paper are based on pair-\nwise comparisons. All p-values were Bonferroni-adjusted for\n36,012 genes, which is equal to the number of genes captured", + "title": "2016 - Genetic networks in mouse retinal ganglion cells.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "ca0d3a29-7814-5d09-ad9d-e4143e87900d", + "extraction_id": "194c0d73-a9b7-5b5e-ac92-7dd689da6fc0", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "dc8fdfb1-539c-5941-bd4d-b595164cce9b", + "score": 0.6535828709602356, + "metadata": { + "text": "GeneNetwork provided the platform for correlation analysis,\nprincipal component generation, and linkage analysis. In\ngeneral, datasets were queried for gene symbols, downloaded\nfrom GeneNetwork, and additional analysis was performed\nin R whenever necessary. P-values mentioned in relation to\nPearsons coecient throughout this paper are based on pair-\nwise comparisons. All p-values were Bonferroni-adjusted for\n36,012 genes, which is equal to the number of genes captured", + "title": "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "version": "v0", + "chunk_order": 323, + "document_id": "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "extraction_id": "b881d0e1-11d4-578d-8560-0106c77d7a23", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "score": 0.6387626363322022, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany 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", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "score": 0.6387483692915231, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany 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", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "33dc52df-73a5-514e-8edb-33ae5046b8af", + "score": 0.638529918281442, + "metadata": { + "text": "network. Cell 9, 12121226 (2014).\n12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic \npathways. N. Engl. J. Med. 0, 16991701 (2009).\n13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: \na review of statistical methods and recommendations for their application. \nAm. J. Hum. Genet. 8, 622 (2010).\n14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional \nnetwork of yeast genes. Science 0, 15551558 (2004).", + "title": "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "extraction_id": "46616368-74e6-5605-9e43-9789e8e1bea1", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6373084403688657, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6338300704956055, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "e17f1d54-7ea8-5a44-95b7-5d07f348574c", + "score": 0.6304648142776015, + "metadata": { + "text": "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\nour next phase as we continue to strive for a more represen-", + "title": "2021 - Lessons learned from the eMERGE Network balancing genomics.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "cd0002dd-dcf1-567a-bf41-61eb0d6d982b", + "extraction_id": "8aecb357-2d62-51f9-9256-6fdf8c73791e", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "d519a13a-b6a0-505d-9a90-dd8f974721b4", + "score": 0.6274190171111227, + "metadata": { + "text": "data available across all contributing consortia will facilitate systematic \nexploration of these correlated phenotypes with more sophisticated \nstatistical methods for joint analysis5254, yielding greater insight into \nthe underlying pathways and genetic networks they represent. As data \nfrom human genetic networks accrue, we will be better placed to test \nwhether there is support for the notion of hub genesthat is, genes \nhighly connected with others in the network, proposed by experi", + "title": "2012 - Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.pdf", + "version": "v0", + "chunk_order": 125, + "document_id": "879c61e9-2efa-550b-b7ca-f88d67eb2199", + "extraction_id": "bc862e34-d30b-5882-9cc9-69f2bce72239", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIGrl5sKA3HUkZ2rgn7crnu6ec7EE", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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].", + "role": "assistant" + } + } + ], + "created": 1728918101, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 147, + "prompt_tokens": 1272, + "total_tokens": 1419 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "2aaaf2f2-8ea8-5f34-82ce-60cdce021b1c", + "score": 0.7398017644882202, + "metadata": { + "text": "Lotan et al. Neuroinformatics of major neuropsychiatric disorders\nWe demonstrated that although these disorders share a rela-\ntively 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-\nment, neural projections and synaptic transmission, the second", + "title": "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "38896019-c47e-5288-88a9-302779568cd3", + "extraction_id": "0749dafa-17cf-5434-aad9-151a128e357b", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "06a4a00d-2b22-557a-b744-e4ac1fa8a5a2", + "score": 0.730652928352356, + "metadata": { + "text": "genetic variation) for any psychiatric disorder\n(Fig. 1), there is sufficient information to drawsome general conclusions.\nThe polygenicity of psychiatric illness\nIn addition to finding specific genes, molecu-\nlar genetics can provide information about theheritability of psychiatric disease, an approach\nthat has led to some important insights about\nthe genetic architecture of psychiatric illness.The degree of SNP sharing among disease cases\nestimates the common, inherited portion of a", + "title": "2015 - Somatic mutation in cancer.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "0801355e-6f92-5526-a0b7-85a2bc859c51", + "extraction_id": "feb6add1-ae89-5c82-8d59-6d4d66ea6779", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "cf9ea924-eb96-5444-9a8b-ed45c932b130", + "score": 0.7138526439666748, + "metadata": { + "text": "of shared and unique genetic factors highlights key gene sets and molecular processesthat may ultimately translate into improved diagnosis and treatment of these debilitating\ndisorders.\nKeywords: major neuropsychiatric disorders, neuroinformatics, cross-species, translational, genetic components,\ngenome wide association studies, enrichment\nINTRODUCTION\nCommon psychiatric disorders including attention-", + "title": "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "38896019-c47e-5288-88a9-302779568cd3", + "extraction_id": "300d8f31-5e42-5c17-a801-2f7afad3995e", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "88756a11-58d2-59ec-8eed-08a96fc24ca0", + "score": 0.7116725444793701, + "metadata": { + "text": "6. D. H. Geschwind, J. Flint, Genetics and genomics of psychiatric\ndisease. Science 349, 1489 1494 (2015). doi: 10.1126/science.\naaa8954 ; pmid: 26404826\n7. S. Cichon et al ., Genomewide association studies: History,\nrationale, and prospects for psychiatric disorders.\nAm. J. Psychiatry 166, 540 556 (2009). doi: 10.1176/\nappi.ajp.2008.08091354 ; pmid: 19339359\n8. A. Battle et al., Genetic effects on gene expression across\nhuman tissues. Nature 550, 204 213 (2017). doi: 10.1038/\nnature24277 ; pmid:\n29022597", + "title": "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "24caaa62-2368-534f-8c42-f088c3409510", + "extraction_id": "82c75078-0fc5-508c-95ba-f2975fdec2c5", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "f771b6cd-babd-56c2-a536-fbafc07c9be7", + "score": 0.7088691765743997, + "metadata": { + "text": "the Psychiatric Genomics Consortium found that the results were highly correlated between \nmethods in a comparison of methods applied across several psychiatric disorders ( Network \nPathway Analysis Subgroup of Psychiatric Genomics Consortium 2015 ). A second \nlimitation of pathway-based analysis is that it is still biased by our incomplete prior \nknowledge of gene function in the etiology of psychiatric illness.\nDespite these challenges, pathway-based analyses have identified biological pathways", + "title": "2019 - Beyond Genome-wide Significance Integrative Approaches to the Interpretation and Extension of GWAS Findings for Alcohol Use Disorder.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "f59b3e10-a887-5708-b520-c5e8adb48dcd", + "extraction_id": "f623501d-c824-5334-98d7-dd599d0c063d", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "fd183495-c22b-5b6e-af12-ec216a838141", + "score": 0.7040727348349316, + "metadata": { + "text": "Lotan et al. Neuroinformatics of major neuropsychiatric disorders\nGENES FROM THE NHGRI-CROSS-DISORDER SET CLUSTER IN THREE\nCO-EXPRESSION MODULES WITH DISTINCT SPATIO-TEMPORALEXPRESSION PATTERNS AND FUNCTIONAL BIASES\nOne of the major properties of genes involved in regulation of", + "title": "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "38896019-c47e-5288-88a9-302779568cd3", + "extraction_id": "b3e6daa0-872e-546c-bee5-873b8f716c77", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "224463d2-e8a3-5a17-ab9b-9d6a39a081b8", + "score": 0.700661301612854, + "metadata": { + "text": "Genet. 2009; 85:847861. [PubMed: 19931040] \nBrownlee DJ, Fairweather I. Exploring the neurotransmitter labyrinth in nematodes. Trends Neurosci. \n1999; 22:1624. [PubMed: 10088995] \nBucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI Jr, Reich T, \nSchmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage \nstudies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994; 55:149158. [PubMed: \n8189735]", + "title": "2017 - Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-response Behaviors in Model Organisms.pdf", + "version": "v0", + "chunk_order": 142, + "document_id": "045eff7e-5ff3-5b0e-9858-76eb8560e9d4", + "extraction_id": "4c500aa5-faeb-5273-83a9-c5c91a27c697", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "18de97fd-e46c-5600-b45d-82de340e0d6b", + "score": 0.6984201253402171, + "metadata": { + "text": "with shared effects on ve major psychiatric disorders: a genome-\nwide analysis. Lancet 381(9875):13711379\nDavis S, Meltzer P (2007) Geoquery: a bridge between the gene\nexpression omnibus (geo) and bioconductor. Bioinformatics\n14:18461847\nde Mooij-van Malsen AJG, Vinkers CH, Peterse DP, Olivier B, Kas\nMJH (2011) Cross-species behavioural genetics: a starting point\nfor unraveling the neurobiology of human psychiatric disorders.\nProg Neuropsychopharmacol Biol Psychiatr 35(6):13831390", + "title": "2014 - Analyzing_gene_expression_data_in_mice_w.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "643f0642-d9c6-52f8-8b86-e469e778c003", + "extraction_id": "848a85f6-382c-54e8-947b-670d71bb0639", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "366961c5-4349-5d93-abf5-203de53a4928", + "score": 0.6969206748557505, + "metadata": { + "text": "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\nof tissue-speci c gene networks.\nMolecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7\nINTRODUCTION\nSeveral psychiatric disorders of developmental origin are char-", + "title": "2022 -Restrepo- Predict impulsivity in children.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "15c3ab55-d6e6-532e-a655-759059ab7c07", + "extraction_id": "10e3b0c3-e7cc-52e9-a6c2-e721a848bae5", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "d7155850-29e4-5fec-b5a2-974f8ead2fef", + "score": 0.6969206748557505, + "metadata": { + "text": "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\nof tissue-speci c gene networks.\nMolecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7\nINTRODUCTION\nSeveral psychiatric disorders of developmental origin are char-", + "title": "2022 - Corticolimbic DCC gene co-expression networks as predictors of impulsivity in children.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "fdecd4db-5e3a-5a3a-8145-28d05392822e", + "extraction_id": "8c7a2723-caa8-5ae1-a47c-c0c889443919", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIGrrCJF0xy80I2fCpFw4lJ55PYWM", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.", + "role": "assistant" + } + } + ], + "created": 1728918107, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 147, + "prompt_tokens": 1216, + "total_tokens": 1363 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "5a61091b-7128-5326-a08c-9e53506eb0f4", + "score": 0.60247802734375, + "metadata": { + "text": "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\nto use computational deconvolution methods to predict cell-type\nproportions from bulk genomics data ( Newman et al. 2015 )). In", + "title": "2018 - Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "89534971-8c50-51ee-b2c4-35957579f911", + "extraction_id": "79e0c3a8-7d1b-5372-a776-7e9a76d09691", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "1de27ae0-e471-5f99-baeb-6d53071de37b", + "score": 0.5995309710223072, + "metadata": { + "text": "Filtering out the latter class of technical difficulty im-proved the recovery of genuine cis-modulated transcripts\nand thus to identify genes that are relevant to further down-stream regulation of gene expression and more complex phe-notypes (Ciobanu et al. 2010 ).\nWilliams also discussed the power of a structured mapping\npopulation in model organisms and presented the Complex4 Funct Integr Genomics (2012) 12:1 9", + "title": "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "extraction_id": "3bdf080c-2715-5acc-bba4-717283851240", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "92e845b4-fbdf-52e8-8ebd-39392ccdfeb7", + "score": 0.5966375470161438, + "metadata": { + "text": "genomic hybridization microarrays (8), can complement RNA expression data \nand result in novel discoveries. With the evolution and maturation of proteom ics, \ncertainly combining serum- or tissue-based patterns of protein expression \nwith RNA expression holds promise. Finally, other rich sources of complex \ndata such as the literature can be used to complement our analysis of microar ray \ndata (39). These analyses face significant challenges with respect to gene", + "title": "2008 - Gene Expression Profiling.pdf", + "version": "v0", + "chunk_order": 1781, + "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "extraction_id": "00906abf-f4ca-53f2-a2b6-20359686e9ec", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "d192b3fd-5ece-570a-a905-f94eef684af2", + "score": 0.5949562191963196, + "metadata": { + "text": "data. To model the functional dependence we shall explore machine learning methods16, such as decision tree methods \nto predict the co-expressed gene profiles. As part of this study and in (E) Future work, see below, we will investigate \nthe benefit of using comparative genomics in helping to lo cate and characterise the regul atory elements and signals. \n \nD(d) Integration and Modelling to infer regulato ry systems co-varying with disease status", + "title": "2005 - Part I Previous Research Track Record.pdf", + "version": "v0", + "chunk_order": 64, + "document_id": "1875d68b-adeb-5f91-8a67-91d881906238", + "extraction_id": "0853c5ab-3d98-565c-ba1f-50e5bd91d14c", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "16baa529-fa53-5760-96b2-38779cab00e0", + "score": 0.59277476646825, + "metadata": { + "text": "derived from complex tissue such as brain show a high level of correspondence24,25. Such\nstructure can be used to inform a new level of neuroscientific investigation that is not\npossible using standard analysis of differential expression2225.\nFor example, one of the first such studies23 showed that gene networks could be used to\nprovide a unifying method of identifying transcriptional targets of human brain evolution in", + "title": "2009 - Neuroscience in the era of functional genomics and systems biology.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "08e29201-f2cc-5fd5-9c28-bc4b8aaaa936", + "extraction_id": "52f30738-038c-58b4-af90-3e1c8735e729", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "38245be7-bd5c-5711-94ba-794c16247aa9", + "score": 0.591386079788208, + "metadata": { + "text": "profiling of a multicellular organism,\" Science, vol. 357, no. \n6352, pp. 661 -667, 2017. \n[68] X. Guo, W. Li, and F. Iorio, \"Convolutional neural networks \nfor steady flow approximation,\" in Proceedings of the 22nd \nACM SIGKDD international conference on knowledge \ndiscovery and data mining , 2016, pp. 481 -490. \n[69] V. Ntranos, L. Yi, P. Melsted, and L. Pachter, \"A \ndiscriminative learning approach to differentia l expression \nanalysis for single -cell RNA -seq,\" Nature Methods, vol. 16,", + "title": "2022 -Madadi- AI RNA.pdf", + "version": "v0", + "chunk_order": 181, + "document_id": "03b9b993-8dd5-5b0d-9493-99fb9a624948", + "extraction_id": "ebd9b396-f870-5c65-9460-7f3da6c11e6c", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "14ac602a-df31-53c4-95cf-6ff078ddec34", + "score": 0.5903836488723755, + "metadata": { + "text": "levels can influence the ability to call differential gene expression\n(Oshlack and Wakefield 2009), we also included, as a feature, the\naverage expression level of the genes in the young samples.\nAll machine-learning algorithms assigned genes to the correct\ntranscriptional change with age 67% 81% of the time on average,\nsignificantly above that of a random classification (50%) (Fig. 3B,C;\nSupplemental Fig. S3B,C ;Supplemental Table S3A,B ). Models de-", + "title": "2019 - Remodeling of epigenome and transcriptome.pdf", + "version": "v0", + "chunk_order": 30, + "document_id": "87ffccee-fc33-5373-948d-67736aa0f069", + "extraction_id": "4e757e70-c73b-59b2-8129-d253c4620f49", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "c810e291-415f-5bee-a54b-1548ff0bacd5", + "score": 0.5893268585205078, + "metadata": { + "text": "DNA. Microarray technology is helpful in capturing biological \ngenetic information to computer data. Computational techniques \ncan be applied on those large set of genetic data of every \nindividuals with or without disease, so that the genes that are \nresponsible for the disease occurrence can be po inted out. \nDifferentially Expressed Genes (DEG) are identified using many \ntechniques. Machine Learning (ML) algorithms plays a \nsignificant role in identifying the distinction between normal", + "title": "2018 - A survey on machine learning approaches in gene expression classification in modelling computational diagnostic system for complex diseases.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "8355d7b5-9da9-5bb8-8a3e-6f77c667599c", + "extraction_id": "c7cd8df0-306c-5b1d-97b8-42410f4b82ed", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "5057d65b-2c37-5344-b757-3af91d22c690", + "score": 0.586599588394165, + "metadata": { + "text": "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", + "title": "2005 -Pomp- GenomeExploitation.pdf", + "version": "v0", + "chunk_order": 1058, + "document_id": "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "extraction_id": "d813f94e-cbde-502a-b387-a5cfd585ecca", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "8a074429-2464-5b19-8eb8-6775d588b24f", + "score": 0.586599588394165, + "metadata": { + "text": "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", + "title": "2006 - Marker Assisted Backcrossing .pdf", + "version": "v0", + "chunk_order": 1058, + "document_id": "5efc1bdf-f847-5eaf-a808-9cf71b9399ce", + "extraction_id": "99f23be3-af56-5ae5-9577-ae940bfd9653", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIGrwafXsxRn06hAraC16E8hpnzWh", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.", + "role": "assistant" + } + } + ], + "created": 1728918112, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 188, + "prompt_tokens": 1276, + "total_tokens": 1464 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "54f0e8c3-0322-51a6-b129-5850d0586c84", + "score": 0.718110935800617, + "metadata": { + "text": "dynamic16,17, and several studies have proposed that impaired\nenhancer activation could be at the origin of disease1821. Besides\ninteracting with nearby promoters, enhancers also engage in\nlong-range interactions. Indeed, it is estimated that approximately\n3540% of all promoter-enhancer interactions are intervened by\nat least one gene22, which makes exact enhancer-target prediction\nchallenging. Long-range enhancers interactions can be identi ed\nby chromosome conformation capture methods23,24.", + "title": "2020 - Skeletal muscle enhancer interactions identify genes controlling whole-body metabolism.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "fa738c86-1026-50f5-aebb-285ec92b209c", + "extraction_id": "1a87b58e-d091-582c-b96d-adac454fdf9d", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "b713e667-ba32-514b-8373-0aebd9702cfc", + "score": 0.6770162774910773, + "metadata": { + "text": "motifs found in its promoter (gene-to-sequence). We will referto the ensemble of these inuence interactions as genenetworks.\nThe interaction between two genes in a gene network does\nnot necessarily imply a physical interaction, but can also referto an indirect regulation via proteins, metabolites and ncRNA\nthat have not been measured directly. Inuence interactions\ninclude physical interactions, if the two interacting partnersare a transcription factor, and its target, or two proteins in the", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "640aa5eb-9b93-541a-ba5a-c1179c157c95", + "score": 0.6645434698734611, + "metadata": { + "text": "~90,000 enhancer-promoter interactions (fig.S36). As expected, ~75% of enhancer-promoterinteractions occurred within the same TAD, and\ngenes with more enhancers tended to have high-\ner expression (Fig. 5B and fig. S36). We inte-grated the Hi-C data with QTLs; surprisingly,\nQTLs involving SNPs distal to eGenes but linked\nby Hi-C interactions showed significantly stron-ger associations (as indicated by the QTL Pvalue)\nthan those with SNPs directly in the eGene pro-\nmoter or exons (Fig. 5C and fig. S37).", + "title": "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "24caaa62-2368-534f-8c42-f088c3409510", + "extraction_id": "213169b2-a4b0-5d5c-a297-c9a5896652ad", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "b6a01191-0181-547f-b37c-139a841296e4", + "score": 0.6513058751043167, + "metadata": { + "text": "histone-modifying proteins, and other factors to regulate polymerase-II activity. Such factors can bind in close prox-\nimity to promoters to influence gene expression. However, \nthere is substantial evidence that additional genetic elements \nreferred to as enhancers play major roles in determining cell-\nspecific patterns of gene expression.\n1517 Initially identified \n>30 years ago, enhancer elements can be located at various \ndistances from promoters, typically between 1 and 50 kilo-", + "title": "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf", + "version": "v0", + "chunk_order": 25, + "document_id": "1526d201-2f4e-5e6c-b2c8-8c825e741401", + "extraction_id": "4c2afa3b-cf31-58ba-8ae8-2bf609f25dbc", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "958ecf38-a371-5a53-920f-b28dddea3fe4", + "score": 0.6482539982644332, + "metadata": { + "text": "involved in the regulation of the target genes of both networks, but that the \ninteraction partners through which this regulation is established differs for both \ntarget genes.", + "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "version": "v0", + "chunk_order": 441, + "document_id": "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "extraction_id": "d2dd2002-c8f6-5e2e-a06a-a8a20268c637", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "ec2195b2-3ecd-5a55-a085-db9bb844f818", + "score": 0.6482315869193335, + "metadata": { + "text": "variants in epigenomic features using a systematic, data-driven approach.\nBioinformatics 31,26012606 (2015).\n13. Schug, J. et al. Promoter features related to tissue specicity as measured by\nShannon entropy. Genome Biol. 6,R33 (2005).14. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter\ninteractome in human cells. Proc. Natl Acad. Sci. USA 111, E2191E2199 (2014).\n15. Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specic gene", + "title": "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "version": "v0", + "chunk_order": 156, + "document_id": "0046a766-21c6-582a-b868-685a24920faf", + "extraction_id": "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "dac1a702-ecf9-5fe8-bb31-ea3c13bc94d9", + "score": 0.6480490775079096, + "metadata": { + "text": "regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA\n110, 1792117926 (2013).\n16. Quang, D. X., Erdos, M. R., Parker, S. C. J. & Collins, F. S. Motif signatures in\nstretch enhancers are enriched for disease-associated genetic variants. Epigenet.\nChromatin 8,23 (2015).\n17. Whyte, W. A. et al. Master transcription factors and mediator establish\nsuper-enhancers at key cell identity genes. Cell153, 307319 (2013).", + "title": "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "version": "v0", + "chunk_order": 157, + "document_id": "0046a766-21c6-582a-b868-685a24920faf", + "extraction_id": "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "c9155893-bf1f-516c-b509-f6d2014d275e", + "score": 0.6478024931071714, + "metadata": { + "text": "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.\nEven so, these engineered transcription factors have not yet been \nfully characterized, and if they are to be used as building blocks for complex gene networks, then knowledge of their in vivo kinetics and", + "title": "2009 - Next generation synthetic gene networks.pdf", + "version": "v0", + "chunk_order": 36, + "document_id": "0d620c5e-a9ae-5b19-851b-37e40292ab8d", + "extraction_id": "38e443bd-610e-5a1d-9f32-082e808d016a", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "55660a79-e4ed-5fc7-8232-aa1401bfd3e8", + "score": 0.6460842893694578, + "metadata": { + "text": "BMC Genomics 2008, 9:310 http://www.biomedcen tral.com/1471-2164/9/310\nPage 10 of 17\n(page number not for citation purposes)A gene regulatory network comprising the regulatory\ninteractions of the significant genes and the significant\nand enriched TFs is shown in Figure 5. Obvious are the\nfive hubs, the core regulatory circuit derived from [17].\nWell-regulated candidates can be identified like Acly and\nFabp4 . Target and regulator at the same time is Ipf1.\nDiscussion", + "title": "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "4060609b-1464-55fa-93cd-fefaf2cac900", + "extraction_id": "c9ae0334-a2f7-5063-81aa-f313c77e4b65", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "a85fbdc3-7bb7-5d61-9d14-e15cc49fc28a", + "score": 0.645416340505104, + "metadata": { + "text": "32. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, et al. Systematic \ndissection of regulatory motifs in 2,000 predicted human enhancers using a massively parallel \nreporter assay. Genome research. 2013:gr. 144899.112. \n \n33. Rands CM, Meader S, Ponting CP, Lunter G. 8.2% of the human genome is constrained: \nvariation in rates of turnover across functional element classes in the human lineage. PLoS \ngenetics. 2014;10(7):e1004 525.", + "title": "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf", + "version": "v0", + "chunk_order": 413, + "document_id": "6acebf19-b80c-5352-8201-99d5634fcc80", + "extraction_id": "7f3f1b6c-9fcd-5e8e-a4e0-d53da591d706", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIGs1N0h1lzkHw7McrwTnV7iXLWUI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.\n\nFor 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.\n\nOverall, 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.", + "role": "assistant" + } + } + ], + "created": 1728918117, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 224, + "prompt_tokens": 1316, + "total_tokens": 1540 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9172db35-cec2-5970-8e5d-d73357f13abe", + "score": 0.6670019626617432, + "metadata": { + "text": "high-throughput sequencing (ATAC-seq) allows the characterization of accessible chromatin re-\ngions,whichcorrespondtoareasoftranscriptionactivity(149).Examiningthethree-dimensional\norganization of the genome can facilitate the association between regulatory elements and\ntheir target genes by dividing the genome into discrete functional blocks, commonly known as\ntopologically associating domains (139). The Encyclopedia of DNA Elements (ENCODE) and", + "title": "2020 - The Genomics of Auditory.pdf", + "version": "v0", + "chunk_order": 58, + "document_id": "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "extraction_id": "0c7a27ef-7a65-5b32-8129-b168a336018a", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "2020244c-6b6e-5613-900e-d7e32f6c4d57", + "score": 0.6658084988594055, + "metadata": { + "text": "variants, it is still unclear how multiple independent variants\ninfluence gene networks through changes in chromatin states.\nThe Assay for Transpose Accessible Chromatin (ATAC-seq)\nwas recently developed to address the need for sensitive as-\nsays requiring less starting material, which also has the ability\nto simultaneously profile open chromatin, transcription factor-\nbinding footprints, as well as nucleosome positioning in a\nsingle assay [ 57]. Given the limited availability of primary", + "title": "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "extraction_id": "203710b7-3267-5ecf-9397-b5becdaeead1", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "f4ae7779-bbfc-5a13-bcd2-2e6724011eb8", + "score": 0.6634333729743958, + "metadata": { + "text": "Data Fig.4a). To relate cell-type-resolved accessible chromatin to gene \nexpression, we created a single-cell RNA sequencing (scRNA-seq) refer -\nence map of peripheral blood and pancreas. We assigned cell-type identi -\nties for 90,495 cells to 29 clusters, which identified similar cell types and \nproportions to snATACseq (Extended Data Fig.5ac).\nTo characterize cis-regulatory programs, we aggregated reads from \ncells within each snATACseq cluster and identified accessible chroma -", + "title": "2021 - Interpreting type 1 diabetes risk.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "extraction_id": "607a959f-6f63-5f18-8935-b76d87aa4820", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "1bdc47f8-9b31-5f89-8381-2238c4aec987", + "score": 0.6631792783737183, + "metadata": { + "text": "DNA methylation and ATAC-seq data (Supplementary Fig. 3). Integration across gene- and coordinate-centric views helps users examine genomic events in different \nchromosome contexts. For example, Xenas \nVisual Spreadsheet can help elucidate whether a gene amplification is part of a chromosomal arm duplication or a focal \namplification (Supplementary Fig. 6).", + "title": "2020 - Visualizing and interpreting cancer genomics.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "eaae9d37-9c40-5e1c-9de9-d5ebcce9eae3", + "extraction_id": "ffc72db8-67ea-508a-aba1-d2592bd00ea2", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "6b16574f-b513-5361-a0a8-a19f86ef6316", + "score": 0.6621679067611694, + "metadata": { + "text": "matin accessibility assay ATAC-seq has been applied to single\ncells and has been shown to capture a higher order chromatin\nstructure resembling the profiles generated by Hi-C [ 72].\nAdditionally, for CAD candidate genes that are transcrip-\ntion factors (TF), such as TCF21 and STAT3, protein-DNA\ninteractions could be studied on a genome-wide scale using\nchromatin immunoprecipitation sequencing (ChIP-Seq).\nRecently, ChIP-Seq performed against TCF21 in human cor-", + "title": "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "extraction_id": "2e588b06-841f-50d7-b161-330199d5c4cf", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "5297cd77-3ccf-570e-9ff9-bdb778638793", + "score": 0.6546493768692017, + "metadata": { + "text": "seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Formaldehyde-\nAssisted Isolation of Regulatory Elements (FAIRE-seq) and DNase I hypersensitive sites sequencing \n(DNase-seq). \nThe integration of DNA methylation data (WGBS) and chromatin accessibility data (ATAC-seq) \nwith established ChIP-seq mark ers have provided an opportunity to create high-resolution", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "9cd48835-a7bf-50aa-928f-adb817e229d4", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "a49d3e49-6005-5890-ba75-8e5d59df13e5", + "score": 0.638329029083252, + "metadata": { + "text": "94. Mumbach MR, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919922. doi: \n10.1038/nmeth.3999.\n 95. Kumasaka N, et al. Fine-mapping cellular QTLs with RASQUAL and ATAC-\nseq. Nat Genet. 2016;48:206213. doi: 10.1038/ng.3467.\n 96. Buenrostro JD, et al. ATAC-seq: a method for assaying chromatin acces-\nsibility genome-wide. Curr Protoc Mol Biol. 2015;109:21.29.121.29.9. \ndoi: 10.1002/0471142727.mb2129s109.", + "title": "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "extraction_id": "786d21d6-5544-5357-8163-1a1a96f6a791", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "eafc949f-7238-5776-bfef-5ccd9f91787e", + "score": 0.6377456378264919, + "metadata": { + "text": "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)\nsequencing data identified sequence interaction with gene promoters,", + "title": "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "b9194555-5fdb-549e-9edb-d108132a7dd1", + "extraction_id": "d26b98eb-66cc-5185-9061-cda1fe904ba6", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "c93bf9e1-39bd-59a9-8dd1-1b67a0853b8c", + "score": 0.6230217218399048, + "metadata": { + "text": "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 \nBysani M etal (2019) ATAC-seq reveals alterations in open chromatin \nin pancreatic islets from subjects with type 2 diabetes. Sci Rep 9:7785. https ://doi.org/10.1038/s4159 8-019-44076 -8\nCamp JG etal (2015) Human cerebral organoids recapitulate gene \nexpression programs of fetal neocortex development. Proc Natl", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 115, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "f740892a-7817-58b0-bec4-8648086b2353", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "6442bc7c-4e2e-553f-82c4-b2f09e01823e", + "score": 0.6181412935256958, + "metadata": { + "text": "genes are involved with multiple biological features. RNA sequencing has been coupled with protein quantication (DNA barcoded antibodies to quantify protein \nexpression) and ATAC-seq to facilitate the study of genes involved with chromatin accessibility remodeling. \ntheir environment [14 , 31 , 88 , 95 , 105] . Advances in multiplexed gene \nediting and transcriptional programing will also enable CRISPR screens", + "title": "2021 - Moving from in vitro to in vivo CRISPR screens.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "31d137b9-90a3-5b5a-ba4f-7a4d5b2c61bc", + "extraction_id": "6078715b-9964-5922-8fc9-5f105d0001ca", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIGs9vl6ZxGFt8u7h4G1USup0nUIZ", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.\n\nAdditionally, 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.\n\nAnother 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].\n\nFurthermore, 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].\n\nThese strategies collectively leverage statistical, computational, and biological insights to improve the inference of causal relationships in gene networks.", + "role": "assistant" + } + } + ], + "created": 1728918125, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 222, + "prompt_tokens": 1308, + "total_tokens": 1530 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "83b84d63-4942-5c91-b93e-3ea1164c600e", + "score": 0.6962268533571462, + "metadata": { + "text": "genetic data which are shifting the paradigm of net work inferences by providing \nstatistical evidence to support directed links betw een genes, proteins, metabolites or \ndiseases. In Chapter 6 , different approaches using genetic data for gene network \ninference that have been proposed are reviewed. Chapter 7 examines the statistical \npotential of such methods under different realistic settings: varying population sizes \nand in the presence or absence of hidden factor var iation and suggests ways to", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 74, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "d0102d97-2e08-50c3-86f4-d1103da9cca1", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "05de9482-4937-5a26-b7fc-0a3cd86c4c40", + "score": 0.6899891680485518, + "metadata": { + "text": "73. Yu,J., Smith,V.A., Wang,P .P ., Hartemink,A.J. & \nJarvis,E.D. Advances to Bayesian network \ninference for generating causal networks from \nobservational biological data. Bioinformatics 20, \n35943603 (2004).\n74. Sachs,K., Perez,O., Peer,D., Lauffenburger,D. A. & \nNolan,G. P . Causal protein signaling networks derived \nfrom multiparameter single cell data. Science 308, \n523529 (2005).\n75. Feizi,S., Marbach,D., Mdard,M. & Kellis,M. \nNetwork deconvolution as a general method to", + "title": "2015 - Quantitative and logic modelling of molecular and gene networks.pdf", + "version": "v0", + "chunk_order": 163, + "document_id": "8db6a373-be03-5653-beaf-1b2ae1d98c31", + "extraction_id": "e23eae56-f71e-55fb-b443-e95adfe8ef22", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "b0b9c2ba-ff4b-5b2c-854f-70007eba8fd4", + "score": 0.6785443212337111, + "metadata": { + "text": "Causal Inference of Regulator-Target Pairs by Gene Mapping 97\n1.2 Background: Inferring Regula tory Networks from Correlated\nGene Expression\nIndependent of the data sets described so far, large collections of gene expres-\nsion over time course (Spellman et al., 1998) or varying environmental con-\nditions (Gasch et al., 2000; Hughes et al., 2000) have been studied to reveal\ndependent variation among genes and thereby deduce regulatory relationships.", + "title": "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "version": "v0", + "chunk_order": 634, + "document_id": "5ded506d-7935-53f9-a118-57a9f3943376", + "extraction_id": "2d776c48-9d99-5feb-9c18-113416c86d96", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "951c0969-df10-5038-b235-1bf4fa358ebb", + "score": 0.6777965618493235, + "metadata": { + "text": "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 [ \n1 ]. These different sources of interaction \ndata can be collated into network models ( see Note 1 ) which \nallow analysis using techniques borrowed from graph theory.", + "title": "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "extraction_id": "3292d5e1-b06c-5041-8190-44119ec0fdf0", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "09527834-da5e-5c34-9439-cf078f40870f", + "score": 0.6764813258361038, + "metadata": { + "text": "relationships with a causal inference test . BMC Genet 2009, 10 :23. \n60. Chaibub Neto E, Ferrara CT, Attie AD, Yandell B S: Inferring causal \nphenotype networks from segregating populations . Genetics 2008, \n179 (2):1089-1100. \n61. Li Y, Tesson BM, Churchill GA, Jansen RC: Critical preconditions for \ncausal inference in genome-wide association studies under review 2010. \n62. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 696, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "f71776c8-e5c9-55e0-ad54-3725550dea19", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "98fdd553-df98-510e-8e0d-62739abf5518", + "score": 0.6709361868538757, + "metadata": { + "text": "T, Samson L, T I (2006) A systems approach to mapping DNAdamage response pathways. Science 312:10541059\nYu J, Smith V A, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to\nbayesian network inference for generating causal networks fromobservational biological data. Bioinformatics 20:35943603How to infer gene networks from expression proles\nM Bansal et al\n10Molecular Systems Biology 2007 &2007 EMBO and Nature Publishing Group", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "452b1ade-c691-5feb-9a12-cfe83ae314af", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "29e3d52a-5651-5cdc-94a7-babb6142e244", + "score": 0.6694398916435371, + "metadata": { + "text": "with the data.\nTo cope with this problem, Siegenthaler et al. proposed a novel\nassessment procedure that incorporates the inferability of gene\nregulatory interactions by redening the confusion matrix interms of inferability of the network, i.e., the possibility of the\nnetwork to be determined from data. The inferability of GRNs\nwas analyzed based on the causal information that could beextracted from experiments. Authors used data from the DREAM", + "title": "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "f64cf13c-d989-50da-be0d-81e34a735a42", + "extraction_id": "b5c98115-372f-5bee-8517-80dc9b6838ee", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "6bda096f-f5e1-51c9-9818-6c13cdfc8fe9", + "score": 0.6672160227766035, + "metadata": { + "text": "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 \nof this type will all be directed edges from gene to phenotype (where the phenotype is the same as used as the seed).", + "title": "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "version": "v0", + "chunk_order": 28, + "document_id": "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "extraction_id": "c2a8a3ab-2531-55c1-920b-d908fa07c027", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "fbae4b79-573c-5b0b-ba0f-3761dbb22590", + "score": 0.6666336456944478, + "metadata": { + "text": "tional methodologies in gene regulatory net-works. IGI Global, Hershey, PA, pp 127\n11. Roy S, Das D, Choudhury D, Gohain GG,\nSharma R, Bhattacharyya DK (2013) Causality\ninference techniques for in-silico gene regu-latory network, Mining intelligence and knowl-edge exploration. Springer, New York,\npp 432443\n12. Olsen C, Meyer PE, Bontempi G (2009) Infer-\nring causal relationships using information the-oretic measures. In Proceedings of the 5th\nBenelux Bioinformatics Conference (BBC09)", + "title": "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "version": "v0", + "chunk_order": 61, + "document_id": "f64cf13c-d989-50da-be0d-81e34a735a42", + "extraction_id": "ae0e55f7-f33c-5179-ba14-8221c2a07be8", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6610835396597672, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIGsGaW9DtpbrFAp6kQFqInDl6kUP", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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]. \n\nAdditionally, 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]. \n\nOverall, 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.", + "role": "assistant" + } + } + ], + "created": 1728918132, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 245, + "prompt_tokens": 1260, + "total_tokens": 1505 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "615beb0f-6b0a-59a6-a2fe-0be884c43d55", + "score": 0.7195009803623257, + "metadata": { + "text": "On the other hand, single-nucleus RNA-seq (snRNA-seq)\nprovides an alternative method for gene expression proling\nin complex tissues from frozen samples at single cell levels\n(Grindberg et al., 2013). Compared to scRNAseq, snRNA-seq\nanalyze gene expression within the nuclei instead of intact cells.\nIt should be noted that there could be potential dierences\nbetween the RNA type and expression levels between nucleus\nand cytosol. As observed in a previous study comparing nuclear", + "title": "2021 - New Technologies to Study Functional Genomics of Age-Related Macular Degeneration.pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "419ee941-2cd6-56ae-8221-aed1c22a8ee2", + "extraction_id": "453f1ace-3591-50a3-afa5-86404632ace3", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "732b8fa8-8832-5002-bea1-bdde2bc61c64", + "score": 0.7094707911891229, + "metadata": { + "text": "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; \nMacaulay etal. 2017; Stuart and Satija 2019). These would", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "60355441-16f5-53a2-9b24-9616624f8d00", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "849c1df7-4164-5164-b3be-6cdeb62ee555", + "score": 0.6886525339691333, + "metadata": { + "text": "of the disease, profiling gene expression in only bulk tissue sam-ples may obscure biologically relevant cell-type specific changes.\nWhile single-cell RNA-seq allows us to evaluate transcriptional\nchanges within cell-types, it is prohibitively costly to executeon large cohorts (i.e. hundreds of individuals). To circumvent\nthis issue, we developed a framework that leverages single-", + "title": "2020 - Integrative genomics approach identifies conserved.pdf", + "version": "v0", + "chunk_order": 23, + "document_id": "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "extraction_id": "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "3069c1d1-6b89-513a-83c3-e64cce07043f", + "score": 0.687616845596339, + "metadata": { + "text": "2019). The traditional RNA sequencing technology (bulk RNA-seq) is\napplied to determine gene expression pro les, isoform expression,\nalternative splicing and single-nucleotide polymorphisms on basis oftissue samples, which contains various cell types ( Kuksin et al., 2021 ).\nOn the contrast, single-cell RNA sequencing (scRNA-seq), a noveltechnology can detect the gene expre ssion patterns for each transcript\nwithin single cell and distinguish cell subtypes ( Lhnemann et al., 2020 ).", + "title": "2023 - Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "8505ccf0-3138-5b83-b36d-8ebd7506a3a4", + "extraction_id": "59e0781d-994c-5ef5-b2f4-073f4a73743b", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "504a960d-e669-52d1-b6c0-439b4f981d5f", + "score": 0.6853773593902588, + "metadata": { + "text": "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,\nwith or without combining with antibodies for cell surface proteins. Transcriptomic analysis by\neither microarray or bulk RNA sequencing then follows (39,67,68,104,145).Such analyses can\n280 Taiberetal.\nAnnu. Rev. Genom. Hum. Genet. 2022.23:275-299. Downloaded from www.annualreviews.org", + "title": "2020 - The Genomics of Auditory.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "extraction_id": "16c769c7-b6ad-5b50-8d81-92c6768595f5", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "769d2c00-d882-59a6-aa69-feb575c9fe1a", + "score": 0.6814767122268677, + "metadata": { + "text": "Recent applications\nSingle-cell RNA sequencing has had a profound impact\non our understanding of neuronal and hematopoietic\ncell types, as well as the immune system. Examples of\nnovel insights in immunity include a window on to an\nunexpected plethora of dendritic cells in mouse immun-\nity [25] and new regulators and subpopulations of CD4+\nT cells [26 28]. In hematopoiesis, much single-cell tran-\nscriptomics work has focused on hematopoetic stem\ncells and the single-cell perspective has provided reso-", + "title": "2016 - Single-cell genomics coming of age.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "dca877e8-cbb9-561e-9b3c-6085228af97d", + "extraction_id": "8d4d3a2d-0aca-5880-98e7-92638c72dd31", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "1fa406bc-fb29-5b60-90bc-1e77bd499df6", + "score": 0.6796850168611117, + "metadata": { + "text": "single- nucleus RNAseq makes them a valuable complement to the find-\nings published by Orozco, Chen et al. (Orozco et al., 2020 ). Furthermore, \nYan et al. (2020) used cell sorting to enrich for cell types with a high \ndegree of heterogeneity, resulting in finer cell subtype resolution for \nnon-photoreceptor cell types such as RGCs. \nIn addition to neural retina, our understanding of the choroidal", + "title": "2022 - Systems genomics in age-related macular degeneration.pdf", + "version": "v0", + "chunk_order": 57, + "document_id": "e8cf1e00-cf22-54cb-a0de-790a822c62d1", + "extraction_id": "e488a94d-d7b3-5d56-bd56-95ac6e89d3ed", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "5f508353-ff30-5dfc-9bac-4bb8c6627391", + "score": 0.672986289053747, + "metadata": { + "text": "using sequencing (ATAC-seq),95,96 that can map chro-\nmatin interactions and accessibility with higher resolu-tion than previous methods will improve our ability to \ndisentangle GWAS loci; while single-cell RNA sequenc-\ning\n97,98 and CRISPR-based pooled gene perturbation \nmethods99103 provide unprecedented opportunities for \nstudies of how RNA expression patterns differ between cells within tissues and how those tissues and cells react \nto perturbation of multiple genes in parallel.", + "title": "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 92, + "document_id": "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "extraction_id": "74048afb-68c3-520a-b661-1d347e9d2fcd", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "42cf70a7-610a-5792-be62-58114dfc505a", + "score": 0.6666386127471924, + "metadata": { + "text": "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\ndatasets.\nWe isolated nuclei and performed single-nuclei RNA-seq\n(snRNA-seq, n= 27 321 nuclei) on postmortem human brain\ntissue from aged, neurologically healthy controls ( n=5 ,6 7t o9 0 +\nyears old, PFC, Supplementary Material, Table S1 ) to clarify cell-\ntype proportions and the corresponding transcriptional profiles", + "title": "2020 - Integrative genomics approach identifies conserved.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "extraction_id": "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "908fad18-f471-5067-8bfc-f49951bdb4d1", + "score": 0.665932834148407, + "metadata": { + "text": "without the biases of probe sequence selection and\nhybridization reactions. The second innovation is cell-specific\nRNA profiling methods [79] that make it practical to generate\ncomparatively accurate expression data for individual cell\ntypes in genetically engineered lines of mice. We can soon\nexpect far more comprehensive and specific lists of genes for\nseveral important cell and tissue types that can be used to\nassemble multicellular expression networks in eye.ACKNOWLEDGMENTS\nDr. Eldon E.", + "title": "2009 - Gene expression in the mouse eye an online resource for genetics using 103 strains of mice.pdf", + "version": "v0", + "chunk_order": 253, + "document_id": "85241c56-1338-5b42-8b33-10b14514f169", + "extraction_id": "65c45e96-da39-59d8-9b9e-0679df8b1472", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIGsO45INZIWjU37FcOiRroinBDZj", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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:\n\n1. 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].\n2. 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].\n3. 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].\n\nWhile 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.", + "role": "assistant" + } + } + ], + "created": 1728918140, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 215, + "prompt_tokens": 1346, + "total_tokens": 1561 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "302feae2-3bab-5fb8-8483-0cea906c83e8", + "score": 0.6653348207473755, + "metadata": { + "text": "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\n53.Zhu J et al. (2008) Integrating large-scale functional genomic data to dissect the complexity ofyeast regulatory networks. Nat Genet 40:854861\n54.Kim JK et al. (2005) Functional genomic analysis of RNA interference in C. elegans. Science308:11641167", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 161, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "29c980ec-bd6e-5ae7-a61c-5abd67d0ef67", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6633270978927612, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "0374a059-20c1-5b75-a7a7-bf69ce03740c", + "score": 0.6616688966751099, + "metadata": { + "text": "expression and its effect on disease . Nature 2008, 452 (7186):423-428. \n12. Chen LS, Emmert-Streib F, Storey JD: Harnessing naturally randomized \ntranscription to infer regulatory relationships amo ng genes . Genome Biol \n2007, 8(10):R219. \n13. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient \nthe edges in quantitative trait networks: the NEO s oftware . BMC Syst \nBiol 2008, 2:34. \n14. Millstein J, Zhang B, Zhu J, Schadt EE: Disentangling molecular", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 606, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "c0983224-1ade-5c10-9f2b-847e9b33f706", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "860be786-e27d-5dd1-96bf-4bcc48957b4d", + "score": 0.6570985317230225, + "metadata": { + "text": "and unknown function by large-scale coexpression analysis.\nPlant Physiol 2008, 147:41-57.\n98. Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals gen-\neral applicability of \"guilt-by-a ssociation\" within gene coex-\npression networks. BMC Bioinformatics 2005, 6:227.\n99. Lee NH: Genomic approaches for reconstructing gene net-\nworks. Pharmacogenomics 2005, 6:245-58.\n100. Goutsias J, Lee NH: Computational and experimental\napproaches for modeling ge ne regulatory networks. Curr", + "title": "2009 - Genes and gene expression modules associated with caloric.pdf", + "version": "v0", + "chunk_order": 316, + "document_id": "893ba204-2e69-563f-9046-7246ca61494f", + "extraction_id": "56129761-d500-59b9-bd9b-cd9cbcada21c", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "4488c0f4-c24a-5b6d-814a-a30b15cc4c03", + "score": 0.6562051177024841, + "metadata": { + "text": "the discovery of interface genes. These mRNA transcripts regulate expression of genes\nin those structures, and thereby couple multiple networks a nd biological processes. The\ndetection of these transcripts and the analysis of their gen es regulatory polymorphisms\n37", + "title": "2009 - Visual analytics for relationships in scientific data (1).pdf", + "version": "v0", + "chunk_order": 242, + "document_id": "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "extraction_id": "d64d8cf5-5b57-5a29-99b4-a8d2ab4bda21", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "9f6fb84a-f487-5ea6-a84e-403642b6d76e", + "score": 0.6557608842849731, + "metadata": { + "text": "Rev. Genet 2007;8:437449. [PubMed: 17510664] A review of theory and approaches to mapping\ngenetic interaction networks.\n16. Bork P, et al. Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol 2004;14:292\n299. [PubMed: 15193308]\n17. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I.\nAccuracy assessment. Genome Res 1998;8:175185. [PubMed: 9521921]", + "title": "2007 - Integrating physical and genetic maps from genomes to interaction networks.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "a9a113e2-d5e5-5903-91de-4b45b37d870f", + "extraction_id": "ba1a83a3-d0e9-5f1e-870f-228abdae771d", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "0858b8f7-66f3-5741-ae7e-4504bca7292f", + "score": 0.6545545664695175, + "metadata": { + "text": "CC represents a dramatic improvement over existinggenetic resources for mammalian systems biology appli-\ncations (Adam et al. 2007 ; Chesler et al. 2008 ). A number\nof gene expression data sets from microarray experiments,particularly those for mouse and rat, have been integrated\ninto GeneNetwork ( http://www.genenetwork.org ), which is\nessentially a web knowledgebase in which the entire dataset and relevant metadata (data about the data) are com-\nbined with sophisticated statistical and computation tools", + "title": "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "27c922c6-e449-5f83-868a-3ad7284facc8", + "extraction_id": "298ee1f5-58a9-567c-86ba-8ac5967e1718", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "a02b4589-65ec-50e1-9849-090971ddb2b0", + "score": 0.6540389060974121, + "metadata": { + "text": "gene, and the first f unctional anti -sense miRNA, Lastly, we have used \ncomparative genomics to infer regulatory networks based on individual \nconserved instances of regulatory motifs, which show functional enrichments \nsimilar and sometimes higher to genome -scale experimental met hods such as \nChIP -chip. As part of the ENCODE and modENCODE projects, we are now \nstudying dynamics of developmental and cell -differentiation networks in", + "title": "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "version": "v0", + "chunk_order": 239, + "document_id": "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "extraction_id": "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "7d3e3705-c5e7-5a37-91c1-a87842f5b9a7", + "score": 0.6538775162064292, + "metadata": { + "text": "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu-\nlatory sites from the cisRED database (Robertson et al. , 2006). There is much to do\nin both of these emerging areas but even preliminary data have already given new\ninsights into mammalian biology: it seems there is high lineage specic expansion of\nsome ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another\ngrowing area of activity is in cataloguing the genetic variation present in human", + "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 499, + "document_id": "045edae8-468b-5725-be06-8cb4b8f6a92b", + "extraction_id": "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "73198d17-f9ce-5528-89d8-f6e466258708", + "score": 0.6538775162064292, + "metadata": { + "text": "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu-\nlatory sites from the cisRED database (Robertson et al. , 2006). There is much to do\nin both of these emerging areas but even preliminary data have already given new\ninsights into mammalian biology: it seems there is high lineage specic expansion of\nsome ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another\ngrowing area of activity is in cataloguing the genetic variation present in human", + "title": "2007 - Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 499, + "document_id": "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "extraction_id": "9c89683f-aca5-57f9-b28d-62e9eb64377b", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIGsUMf0eTPsxD8TBs1unBQPLXIKg", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.", + "role": "assistant" + } + } + ], + "created": 1728918146, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 136, + "prompt_tokens": 1062, + "total_tokens": 1198 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6523095965385437, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "score": 0.6367458701133728, + "metadata": { + "text": "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; \nGlade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; \nRubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of \ntumors.\nMethods\nGeneNetwork and WebGestalt\nGeneNetwork is an open access, online data analysis resource for systems biology and \nsystems genetics. It contains a large number of microarray datasets from multiple tissues of", + "title": "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "extraction_id": "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "6beb1115-9f40-555f-a6b4-3c73945101a0", + "score": 0.636005699634552, + "metadata": { + "text": "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", + "title": "2015 - Identification of candidate genes that underlie the QTL on chromosome 1 that mediates genetic differences in stress-ethanol interactions.pdf", + "version": "v0", + "chunk_order": 25, + "document_id": "eecf4236-efca-577d-ba62-c20c9768950e", + "extraction_id": "26045fea-cd20-5e3d-be07-e8a8e9ca603a", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "6e2695ed-e652-52e1-b896-0bbbb585bb60", + "score": 0.6356210302001548, + "metadata": { + "text": "ject to mapping analysis. We examine the connectivity among these sets and analyze\nthe molecular, biochemical and genetic regulatory commonality of connected genes us-ing novel and existing bioinformatics tools. We also develop data-driven hypotheses to\nexplain the mechanisms of genetic perturbations and variation as a means of dening\nglobal consequences of individual differences on tissue structure and function.\nMuch of our work is motivated by prior studies of brain gene expression and mRNA", + "title": "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "d9038328-bfea-5f73-87aa-6077b697e4db", + "extraction_id": "f1181fc1-fe08-53b1-bda7-00423a568234", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "score": 0.6329202870774109, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany 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", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "score": 0.6328813275240315, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany 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", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "bd4b772b-4df4-588e-a7bd-2d5d9484f945", + "score": 0.6269146203994751, + "metadata": { + "text": "weighted gene co-expression network are described in[54]. Consensus network analysis was carried out with Rfunction blockwiseConsensusModules in the WGCNA R\npackage [54].\nOur online R software tutorial easily permits the user\nto identify tissue-specific age related modules and CpGs.\nGene ontology enrichment analysis", + "title": "2012 - Aging effects on DNA methylation modules.pdf", + "version": "v0", + "chunk_order": 163, + "document_id": "cdd3bf57-3c36-5673-bd78-1e53f384d539", + "extraction_id": "bf37d9e2-c9a3-5886-88db-103264c4cecb", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "9bf34d9a-9c54-5376-a38e-7f32daba8107", + "score": 0.6185001730918884, + "metadata": { + "text": "approach employed in the construction of large expression data\nsets, such as those provided by GeneNetwork,39treats gene\nexpression as a continuous variable across RI strains, rather than asa categorical one (knockout model). Hence, we believe that using\nthese complementary, yet conceptually distinct, approaches\nenhanced our ability to propose mechanistic insights.\nA limitation of the current study relates to the non-trivial\nrelationship between structural and functional brain connectivity.4", + "title": "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", + "version": "v0", + "chunk_order": 162, + "document_id": "8cd3e767-17b8-5868-b335-fdb6cc2ff02c", + "extraction_id": "ea5fd027-559f-568f-9c4d-a4615730426a", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "225f0aa2-c185-5b36-923a-a24e545b866f", + "score": 0.6179440845284423, + "metadata": { + "text": "GeneNetwork ( http://www.genenetwork.org ; Williams and Mulligan, 2012)). These databases 180 \nrepresent transcriptome datasets for different tissues of recombinant inbred mice. If several probes 181 \nfor the same gene were available, probes with higher maximum likelihood ratio statistic (LRS, a 182 \nmeasurement of the association or linkage between differences in traits and differences in particular 183 \ngenotype markers values) were used. 184", + "title": "2018 - Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "e4b2f5dc-6df6-5af8-9ca9-3ccb6518d300", + "extraction_id": "434963e5-549e-5986-90a9-cbf4a5f7f06e", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "b6b401f6-66c1-5e0d-ab68-09f6f6d7e10f", + "score": 0.6176329255104065, + "metadata": { + "text": "pathways.TheGeneNetworkdatabaseisauniqueresourceforco-expressionanalysisusingdatafromavarietyof\ntissues acrossgeneticallydistinctinbredmice.However,extractionofbiologicallymeaningfulco-expressedgenesets\nischallengingduetovariabilityinmicroarrayplatforms,probequality,normalizationmethods,andconfounding\nbiologicalfactors.Inthisstudy,wetestedwhetherliteraturederivedfunctionalcohesioncouldbeusedasanobjectivemetricinlieuofgroundtruthtoevaluatethequalityofprobesandmicroarraydatasets.", + "title": "2019 -Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "0a22eed8-cdda-52de-a73f-d82b3f73b78d", + "extraction_id": "dab0ce13-0d90-514c-9220-8edd64eceb6c", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIGsZPO5vngTlUtdx2Hky3vN1AZqv", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent developments in quantitative trait locus (QTL) mapping have significantly refined our understanding of gene regulatory variants in several ways:\n\n1. **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.\n\n2. **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.\n\n3. **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].\n\n4. **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].\n\n5. **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].\n\nOverall, these advancements in QTL mapping enhance our understanding of the complex regulatory mechanisms underlying gene expression and trait variation.", + "role": "assistant" + } + } + ], + "created": 1728918151, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 412, + "prompt_tokens": 1259, + "total_tokens": 1671 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "93d79fcd-e841-5cd0-baa7-69ad032cacef", + "score": 0.766341714103023, + "metadata": { + "text": "to as quantitative trait loc us (QTL) mapping study. QTL studies inform us region s on the \nchromosome where existing polymorphisms or SNPs are highly correlated with variation of the \ntrait of interest. With the advancement in DNA sequencing, whole genome database of several \nmouse strains as well as gene expression data from several tiss ues are available. This allows us to \nuse bioinformatic tools to identify candidate genes with greater confidence for further functional \nvalidations .", + "title": "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf", + "version": "v0", + "chunk_order": 217, + "document_id": "de8dda5e-0e2f-5aa9-bb13-851c526b36a5", + "extraction_id": "16fdf35c-ab83-53db-9f76-e817326c6067", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "248ac859-2589-5afa-824d-e1357bf23e59", + "score": 0.7579537178305676, + "metadata": { + "text": "differences, allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation. QTL mapping of gene expression traits \nallows us to identify eQTLs; genomic regions that have a regulatory effect on \nthose expression traits. Two types of eQTLs can be distinguished, i.e., those \nthat map near (less than 10 Mb from) the gene which encodes the transcript \n(local ) and those that map elsewhere in the genome ( distant ).\n18 Together, local", + "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "version": "v0", + "chunk_order": 266, + "document_id": "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "extraction_id": "76e22011-da6d-5af7-a74f-2b4d0f11e879", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "32497309-fb89-58c9-b828-6a16fa55c11d", + "score": 0.7529518449988656, + "metadata": { + "text": "simultaneously. Beginning with a study in yeast (Brem et al. 2002), QTL mapping has been \ndone 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 \nhypotheses for further testing and construct regulatory networks in s ilico.", + "title": "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "version": "v0", + "chunk_order": 181, + "document_id": "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "extraction_id": "957166a3-0298-5324-a24a-02b59ec3427f", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "ea5eaca1-c91d-59f6-af5b-5490749d950a", + "score": 0.7513671665568968, + "metadata": { + "text": "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), \nthat promise to uncover pathways of genetic control within the tissue studied.\nTraditionally, QTL mapping starts with a phenotype of inter-", + "title": "2009 - Genetics of the hippocampal transcriptome in mouse a systematic survey and online neurogenomics resource.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "33437cc7-ee4e-59b9-b3e6-ed50eb504b52", + "extraction_id": "a47731b3-bb43-5d9c-a7eb-bfea5eea557e", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "56ba9ce8-4cdd-5d4d-83c1-a370e9c8f959", + "score": 0.7499000281274282, + "metadata": { + "text": "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\nmethods are applied in an effort to identify multiple-QTL regulatory models for large\ngroups of genetically co-expressed genes, and to extrapolate the consequences of thisgenetic variation on phenotypes observed across levels of biological scale through the", + "title": "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "d9038328-bfea-5f73-87aa-6077b697e4db", + "extraction_id": "47c06e52-1923-58d0-9286-9674893a502a", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "cd33f83f-d19c-5419-a157-c2f1d8148347", + "score": 0.7476117616625401, + "metadata": { + "text": "distal regions into even finer regulatory loci. This influence on gene expression may be \nthe reason why so many classical QTLs have been mapped to Qrr1 . \n \nThe complexity highlighted by Qrr1 may very well be the rule rather than the \nexception for loci that modulate complex traits. Efforts to fine -map a single QTL have \noften been confronted by clusters of multiple small effect QTLs within the original \ninterval (Legare et al., 2000; Demarest et al., 2001) . This poses a serious challenge, and", + "title": "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf", + "version": "v0", + "chunk_order": 614, + "document_id": "3d0df5a3-7d7c-5edc-b94d-cae582f59c12", + "extraction_id": "3296b30e-7dd3-576d-a2df-442406caa472", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "e5354b88-c1ec-54e1-ab61-c30689e30ea1", + "score": 0.7471424642631507, + "metadata": { + "text": "genotypes, availing of genetic markers across the whole\ngenome, and allow the identication of QTLs with signi-\ncant effects on the disease (Darvasi 1998 ; Manolio 2010 ).\nQTLs are genetic regions closely linked to a gene with a\nquantitative effect on the phenotype. QTL mapping is\nbased on the concept that phenotypic differences between\ninbred mouse strains can be used to demonstrate theimportance of genetic effects on complex phenotypes\n(Andreux et al. 2012 ; Hillebrandt et al. 2002 ). The standard", + "title": "2015 - Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "464d119c-ba16-5716-8b69-169940f090b4", + "extraction_id": "121f6744-a773-5a59-b8c7-7e7e85e2b067", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "3fa64113-fa70-575c-81ae-0769dff93a27", + "score": 0.7451775377777227, + "metadata": { + "text": "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\ndata 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\ninsight to physiological consequence. These advances will underpin efforts to translate the findings through development of diagnostic tests, ris k evaluation and", + "title": "2008 - Type 2 diabetes new genes, new understanding.pdf", + "version": "v0", + "chunk_order": 91, + "document_id": "3992c53c-c48c-597d-8d96-70b1026deb70", + "extraction_id": "31a1546b-c160-5b22-a3fb-1e26ab2861c3", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "662c7b64-e34e-5faa-b920-6b59334ef372", + "score": 0.7442948748801843, + "metadata": { + "text": "illustrating the potential of functional mapping for effici ently establishing \nassociations between existing QTL, as well as for novel QTL discovery.\nReferences\n1. Damerval C, Maurice A, Josse JM, De Vienne D: Quantitative trait\nloci underlying gene product va riation: a novel perspective\nfor analyzing regulation of genome expression. Genetics 1994,\n137:289-301.\n2. Brem RB, Yvert G, C linton R, Kruglyak L: Genetic dissection of\ntranscriptional regulation in budding yeast. Science 2002,\n296:752-755.", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "9b24b22c-0f8c-5b3c-9479-518aa6dfab01", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "f9ca5851-0871-54ae-8d01-752c806bd081", + "score": 0.7398491966994066, + "metadata": { + "text": "over a decade ago, using new genometypes for the BXD family of murine strains, as well\nas new statistical tools, showing that we can identify new quantitative trait loci (QTLs),\nresulting in highly plausible candidate genes.\nQuantitative trait locus (QTL) mapping has been carried out in numerous species to associate\nregions of the genome to phenotypes even before the structure of the genome was well understood\n(e.g., [ 3]). Rodents, especially mice, have been the species most prominently used for biomedi-", + "title": "2022 -Chunduri- Drugs Animal Models.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45", + "extraction_id": "b93f76a6-6928-564a-a9a0-149c8ef774d9", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIGsiGGBCVBLOOrTQrnGlB4EM7iVd", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org has several implications:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nOverall, 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.", + "role": "assistant" + } + } + ], + "created": 1728918160, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 336, + "prompt_tokens": 1318, + "total_tokens": 1654 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a87fa6ff-4bc0-50ac-b654-f7d734bbbf02", + "score": 0.6593109369277954, + "metadata": { + "text": "frequent usage of terms like epigenetic or chromatin land-scape. New methods for high-throughput mapping ofgenome-wide histone modifications and protein-DNA inter-\nactions were developed over the last few years (Blecher-Gonen\net al., 2013; Garber et al., 2012).\nHistone Modifications Associated with Gene\nEnhancersChromatin can be modulated by covalent histone modifica-", + "title": "2016 - Next Generation Transcriptomics.pdf", + "version": "v0", + "chunk_order": 48, + "document_id": "56cafe26-2b36-50d6-a5c7-c7d947473b61", + "extraction_id": "b3a2e390-fad2-5afc-b8ca-6f0b57b9cac1", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "66fa4c5e-0b26-5c01-b5ec-d199a4da11bb", + "score": 0.6519500613212585, + "metadata": { + "text": "orative efforts of the ENCODE Project [ 42] and Roadmap\nEpigenomics [ 43] consortia have already revealed a\ncompendia of genome-wide histone modification signatures\nfor various regulatory features in multiple primary tissues\nand cell lines. These datasets have been applied to global\nmapping studies and databases to prioritize functional regula-\ntory variants [ 44,45]. While these assays have been employed\nextensively in LCLs, and tumor cell lines to follow-up auto-", + "title": "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "version": "v0", + "chunk_order": 33, + "document_id": "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "extraction_id": "203710b7-3267-5ecf-9397-b5becdaeead1", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "77ae8cce-6686-5930-a6a1-291143cba4c5", + "score": 0.6497661858657721, + "metadata": { + "text": "genetical genomics) and the genetics of epigeneticscould be studied simultaneously, thus revealing genes\nthat directly or indirectly affect epigenetic gene states.\nAn additional issue that could be addressed by such anapproach is to estimate the percentage of variation in\ngene expression that can be explained by different\nepigenetic conformations.\nThe level of complexity could be further increased by\nincluding different cell types in the analysis, such as the", + "title": "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf", + "version": "v0", + "chunk_order": 86, + "document_id": "af6e0103-849d-542f-bca7-0251082bc0b3", + "extraction_id": "767fd341-f407-5322-a932-9b1cecb869e0", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "9c31e888-0660-507d-927f-e54f98a7248f", + "score": 0.6428762674331665, + "metadata": { + "text": "Incorporating epigenetics into genetic analysis can also enhance the predictive\nfunctional analysis of SNPs by highlighting regions of DNA that are accessible or\ninaccessible to protein binding by transcription factors and other regulatory pro-\nteins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG)\nmethylation sites. Rakyan et al. (2004) suggested that such an event might affect the\noverall methylation prole of a locus and, consequently, promoter activity and gene", + "title": "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "version": "v0", + "chunk_order": 1274, + "document_id": "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "extraction_id": "9515bd16-96d4-5b09-b23c-63a1cc5d19ae", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "5935ee2f-4621-577d-8d9b-e47d2d0699e2", + "score": 0.642857394048132, + "metadata": { + "text": "Incorporating epigenetics into genetic analysis can also enhance the predictive\nfunctional analysis of SNPs by highlighting regions of DNA that are accessible or\ninaccessible to protein binding by transcription factors and other regulatory pro-\nteins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG)\nmethylation sites. Rakyan et al. (2004) suggested that such an event might affect the\noverall methylation prole of a locus and, consequently, promoter activity and gene", + "title": "2007 - Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 1274, + "document_id": "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "extraction_id": "522373ca-3ce6-5fe5-b062-ee097f378397", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "0f00daa0-2bb4-5a3f-8d51-a1cd2957bef4", + "score": 0.6428248882293701, + "metadata": { + "text": "Incorporating epigenetics into genetic analysis can also enhance the predictive\nfunctional analysis of SNPs by highlighting regions of DNA that are accessible or\ninaccessible to protein binding by transcription factors and other regulatory pro-\nteins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG)\nmethylation sites. Rakyan et al. (2004) suggested that such an event might affect the\noverall methylation prole of a locus and, consequently, promoter activity and gene", + "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 1274, + "document_id": "045edae8-468b-5725-be06-8cb4b8f6a92b", + "extraction_id": "c5468773-a09b-510d-bcdf-f685d7714106", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6355242133140564, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "03e25c07-34a0-5b1f-a5f9-ba9a0e2c0d91", + "score": 0.63309069374474, + "metadata": { + "text": "374. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F ., Ren, B. et al.\n(2010), The NIH Roadmap Epigenomics Mapping Consortium, Nat.\nBiotechnol. V ol. 28, pp. 10451048.\n375. Portela, A. and Esteller, M. (2010), Epigenetic modications and\nhuman disease, Nat. Biotechnol. V ol. 28, pp. 10571068.\n376. Esteller, M. (2007), Cancer epigenomics: DNA methylomes and\nhistone-modication maps, Nat. Rev . Genet. V ol. 8, pp. 286298.\n377. Gilad, Y ., Rifkin, S.A. and Pritchard, J.K. (2008), Revealing the archi-", + "title": "2011 - Human genetics and genomics a decade after the release of the draft sequence of the human genome.pdf", + "version": "v0", + "chunk_order": 475, + "document_id": "6d475ac7-7094-5268-96ce-ae8f50f42cd2", + "extraction_id": "3960aec4-df25-57cd-9c60-5561f876a795", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "2e2d861b-4662-5ba5-80e6-ff0e4d9e80b4", + "score": 0.6326631903648376, + "metadata": { + "text": "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.\n[Supplemental material is available online at http:/ /www.genome.org. The ChIP-seq and gene expression data from this\nstudy have been submitted to ArrayExpress (http:/ /www.ebi.ac.uk/microarray-as/ae/) under accession nos. E-MTAB-189\nand E-MTAB-191, respectively.]\nGenetic and epigenetic factors determine cell fate and function.", + "title": "2010 - Genome-wide analysis of histone modifications.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "68bfce04-818d-5122-91c2-13a4a3ba0229", + "extraction_id": "6b5317f7-aa3f-5dfe-8e50-ef90619b6707", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "47eea0dd-b899-5ed2-8b16-150b976f1f0a", + "score": 0.6322832703590393, + "metadata": { + "text": "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 . \n Histone Modifi cation Analysis \n Histone modi cation signals can be captured with chromatin immunoprecipita-\ntion (ChIP), which provides modi cation position approximation on the genome", + "title": "2011 - Molecular Genomic Research Designs.pdf", + "version": "v0", + "chunk_order": 151, + "document_id": "ced08e27-8655-59a4-bf63-0ba746f139b7", + "extraction_id": "d3fe612e-6d4a-5410-9e60-cd2ef8fff897", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIGsp8i4dh5GAWf5RuA0Au0DHwVdV", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent findings on 3D genome organization have significantly enhanced our understanding of functional genomic networks in several ways:\n\n1. **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.\n\n2. **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.\n\n3. **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].\n\nThese 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.", + "role": "assistant" + } + } + ], + "created": 1728918167, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 259, + "prompt_tokens": 1395, + "total_tokens": 1654 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "996bbbd1-c605-5733-bf20-42367be2244c", + "score": 0.6783368991814304, + "metadata": { + "text": "genomes. Hence, chromosomal and spatial co-localization in\nthe nucleus may indicate co-regulation. It was previously shown\nthat 3D chromatin structure couples nuclear compartmentaliza-tion of chromatin domains with the control of gene activity ( Gue-\nlen et al., 2008 ) and thus contributes to cell-specic gene\nexpression ( Zullo et al., 2012 ). In this context, it is noteworthy\nthat cellular senescence is associated with modications of theglobal chromatin interaction network ( Chandra et al., 2015 ). To", + "title": "2015 - Insights into Sex Chromosome Evolution and Aging from the Genome of a Short-Lived Fish.pdf", + "version": "v0", + "chunk_order": 63, + "document_id": "def32424-2f9d-5c4b-9c03-be2d8bd53a24", + "extraction_id": "516bdde0-cafe-5eac-a627-32ef9e262e32", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "c7834fbb-eb9a-5f55-ac26-67af248e7179", + "score": 0.665553887751468, + "metadata": { + "text": "2 \n \nIntroduction \nRecent scientific advances have enabled the identification of functional genomic elements \nthrough a diverse set of functional annotations, including proteins functional scores (1, 2) , \nevolutionary conservation scores (3-5), and epigenetics scores from the Encyclopedia of DNA \nElements (ENCODE) (6). Other initiatives such as the R oadmap Epigenomics project (7) and \nFANTOM5 project (8, 9) also provide evidence for potential regulatory v ariants in the human", + "title": "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "6acebf19-b80c-5352-8201-99d5634fcc80", + "extraction_id": "9309edf2-5e2d-5567-ae78-f6681b866410", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "score": 0.6593453491124315, + "metadata": { + "text": "accuracy of predictive networks [40, 5153]. We have also recently demonstrated\nhow this class of network can be used to inform associations identied in GW Astudies [40].\n9 Summary\nThe 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", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "5543c99f-6542-55b8-b62c-e34a03b9c2fe", + "score": 0.6573882903225261, + "metadata": { + "text": "a growing awareness that the three-dimensional juxtaposition of DNAregions within nuclei means that genes can be regulated by regulatory\nelements that are located at some distance from the gene ( Fig. 5 )\n(Javierre et al., 2016 ;Kadauke and Blobel, 2009 ). As a result of this,\ndisease associated SNPs have been shown to fall in gene regulatory\nelements ( Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014;\nLee et al., 2014; Schierding et al., 2015 ).", + "title": "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "extraction_id": "5b8b3673-7fd4-5989-9982-a6d5ea374c8d", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "33dc52df-73a5-514e-8edb-33ae5046b8af", + "score": 0.6539934690006413, + "metadata": { + "text": "network. Cell 9, 12121226 (2014).\n12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic \npathways. N. Engl. J. Med. 0, 16991701 (2009).\n13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: \na review of statistical methods and recommendations for their application. \nAm. J. Hum. Genet. 8, 622 (2010).\n14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional \nnetwork of yeast genes. Science 0, 15551558 (2004).", + "title": "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "extraction_id": "46616368-74e6-5605-9e43-9789e8e1bea1", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "e26ebc1e-e05d-56fb-8718-604275994a84", + "score": 0.6511586904525803, + "metadata": { + "text": "Processing Large-Scale, High-Dimension Genetic 325\nanother. We anticipate these types of networks becoming increasingly important in\nthe 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\nthat cause disease. The integration of genotypic and expression and other data have\nrecently been shown, in a Bayesian network framework [76], to enhance the overall", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "fb3452e6-4584-5c3f-92de-9bc44d30b21c", + "score": 0.6485294136054384, + "metadata": { + "text": "regions correlated with functional noncoding elements, including enhancers, better than did\nregions identified solely on the basis of nucleotide sequence. These results support the idea that\nthe molecular shape of DNA is under selection and can identify evolutionary history.\nGenomic sequences that code for proteinsare relatively well understood but make\nup only ~2% of the human genome ( 1).\nMany functions are encoded in the remaining\n~98% noncoding portion of the genome, but little", + "title": "2009 - Rare Variants of IFIH1, a Gene Implicated in Antiviral Responses, Protect Against Type 1 Diabetes.pdf", + "version": "v0", + "chunk_order": 48, + "document_id": "7d9d5ce7-7bfe-5fe2-a325-fe97db015a10", + "extraction_id": "9893879f-6b73-5dc6-b274-f48ba8163644", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "a02b4589-65ec-50e1-9849-090971ddb2b0", + "score": 0.6415468071969278, + "metadata": { + "text": "gene, and the first f unctional anti -sense miRNA, Lastly, we have used \ncomparative genomics to infer regulatory networks based on individual \nconserved instances of regulatory motifs, which show functional enrichments \nsimilar and sometimes higher to genome -scale experimental met hods such as \nChIP -chip. As part of the ENCODE and modENCODE projects, we are now \nstudying dynamics of developmental and cell -differentiation networks in", + "title": "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "version": "v0", + "chunk_order": 239, + "document_id": "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "extraction_id": "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "38405f05-93df-579e-a8e4-c6b0c13e86a6", + "score": 0.639809391509928, + "metadata": { + "text": "References\n1. Cremer T, Cremer M, Dietzel S, Muller S, Solovei \nI, Fakan S. Chromosome territoriesa function-al nuclear landscape. Curr Opin Cell Biol 2006; 18:307-16.\n2. Misteli T. Beyond the sequence: cellular organization of genome function. Cell 2007; 128:787-800.\n3. Schneider R, Grosschedl R. Dynamics and interplay of nuclear architecture, genome organization and gene expression. Genes Dev 2007; 21:3027-43.", + "title": "2009 - Loss of A-type lamins and genomic instability.pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "eab22335-5688-5e37-9f65-c4b58d6d95c8", + "extraction_id": "a6f16fce-1813-5d38-899f-6eb04c7d0007", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "67365e1f-c588-56b5-aae0-44604958f8e1", + "score": 0.6392257428630755, + "metadata": { + "text": "enhancers in the control of cell identity and disease. Cell(2013) 155:934 47.\ndoi: 10.1016/j.cell.2013.09.053\n45. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear\norganization. Genes Dev (2012) 26:11 24. doi: 10.1101/gad.179804.111\n46. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of\nchromosome architecture. Nat Rev Mol Cell Biol (2016) 17:743 55.\ndoi: 10.1038/nrm.2016.104\n47. Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, et al.", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 171, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "f4955281-f174-562e-bc8a-170b701beffc", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIGsxUUcXG8q6ZckzX5v3uoIBTYQl", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Artificial intelligence (AI) has several potential applications in improving the annotation and interpretation of gene networks:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\n4. **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].\n\nThese 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.", + "role": "assistant" + } + } + ], + "created": 1728918175, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 295, + "prompt_tokens": 1164, + "total_tokens": 1459 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "df726361-271a-5dbb-b6d1-03dab5a63006", + "score": 0.6485917568206787, + "metadata": { + "text": "[111], and for generation of networks based on known gene \ninteractions 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-\ncation by regression (NIR) [119] has been used to identify", + "title": "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "extraction_id": "53c57cc4-4d43-505a-974c-442d06e144df", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "ee9014b2-ff70-50d1-a022-7a5792383700", + "score": 0.6399985933303006, + "metadata": { + "text": "Here we will focus on gene network inference algorithms\n(the inuence approach). A description of other methods\nbased 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\nand Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al,\n2006). We will also briey describe two improper reverse-engineering tools (MNI and TSNI), whose main focus is not", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "6d8b4af6-6baf-58ff-9e1d-003862f53edd", + "score": 0.6375796984904918, + "metadata": { + "text": "NIA[360] may help to infer a putative function by linking unkn own\ngenes to genes known from previous studies to show a similar e xpres-\nsion pattern. We can also characterize unknown genes by thei r evolu-\ntionary, loss-of-function and network interaction proper ties to prioritize\ncandidate variants[184] and even predict disease inherita nce mode to a\ncertain degree[153].\nTaking this approach a step further, GeneNetwork[99] is con structed", + "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "version": "v0", + "chunk_order": 99, + "document_id": "ef974b09-4ea2-5382-85e5-c2169f440fda", + "extraction_id": "223e442e-898d-5aea-866a-5cdc0ac915e8", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "e8279254-6a66-5be6-b6ae-c11c20e242f9", + "score": 0.6360610344373575, + "metadata": { + "text": "network inference techniques can be utilized to infer biologicalprocess and the potential phenotypic impact of variants in genes of\nunknown function [71 78]. Thus, pathway and network based\nannotation approaches can be powerful approaches to inferring\nphenotypic information where direct links to phenotype do not exist.\n2.12. De novo association analyses involving multiple genomes\nIn the absence of prior information one might leverage to annotate", + "title": "2011 - Annotating individual human genomes.pdf", + "version": "v0", + "chunk_order": 78, + "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", + "extraction_id": "070421c2-5d23-58b3-9d85-53dd58e7abae", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "137c8fc7-7bc2-543f-a43e-7f819eaaaaa9", + "score": 0.6275167313198216, + "metadata": { + "text": "interaction may be difficult to quantify. Conversely the\ndirections and signs that accompany signalling or regula-\ntory pathways are generally known, but their incorpora-\ntion requires more work. It could nevertheless lead to\nimportant advances for the interpretation of microarray\ndata in cancer studies, for example.\nConclusion\nWe have presented a general framework to analyse gene\nexpression data when a gene network is known a priori .\nThe approach involves the attenuation of the high-fre-", + "title": "2007 - Classification of microarray data using gene networks.pdf", + "version": "v0", + "chunk_order": 83, + "document_id": "639e0456-a445-5e2e-adf5-8eaf987ce2d1", + "extraction_id": "df700ffb-556a-5331-afe6-71f7e77a1fb8", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "394f5f79-0592-52ff-bc83-ea55a95fd17e", + "score": 0.626812219619751, + "metadata": { + "text": "A number of techniques have been proposed for network inference.\nExisting techniques for nding gene networks can be broadly cate-gorized as (i) computational approaches, and (ii) literature-based\napproaches. The computational approach mainly uses statistical,\nmachine learning, or soft-computing techniques [ 14,15] as discov-\nery tools. On the other hand, a literature-based approach gathers\nrelevant published information on genes and their interrelation-", + "title": "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "f64cf13c-d989-50da-be0d-81e34a735a42", + "extraction_id": "c15261b7-54b9-534f-ac95-17c7a5543f31", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "b54b5584-344c-54e5-9442-a7deb099bc76", + "score": 0.6264445781707764, + "metadata": { + "text": "addition, data from linkage or association studies (e.g. GWAS), or from high -throughput genetic screening \nexperiments (e.g. CRISPR screening), or from animal gain -or-loss- of function studies, or from the gene -drug \ninteractions, can also be exploited to predict potential gene functions. Integration of GeneBridge with data from \nthese sources will further enhance the performance for gene function prediction, as is done in STRING [253], \nGeneMANIA [254] and Mitocarta [190, 255].", + "title": "2019 - Systems genetics approaches to probe gene function.pdf", + "version": "v0", + "chunk_order": 494, + "document_id": "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "extraction_id": "f46459a1-592e-5d14-a6d1-f93211353db0", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "09f8c37f-b150-5f07-8275-bd040787f514", + "score": 0.6230763382636412, + "metadata": { + "text": "include the deep learning-driven pattern recognition models for analyzing the gene se-\nquences for identifying the possible future illness and developing mobile applications that\ncan generalize the information from the genomic data. However, there is great demand for\nexplainable Articial Intelligence models that are interpretable in decision-making.\nAuthor Contributions: The authors contributions are as follows, Conceptualization of the study,", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 234, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "29c89d19-3215-54dc-9723-85f96de02b65", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "3152b693-2396-5441-b6ff-6a80eac13ad0", + "score": 0.6213001834571263, + "metadata": { + "text": "Gene network inference algorithms are becoming accurate\nenough to be practically useful, at least when steady-state gene\nexpression data are available, but efforts must be directedin assessing algorithm performances. In a few years, gene\nnetwork inference will become as common as clustering for\nmicroarray data analysis. These algorithms will become moreTable IV Results of the application of network inference algorithms on the experiment data sets\nData sets ARACNE BANJO NIR Clustering Random", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "d4d71d8c-ef2f-5ddb-b3f3-0f5ce8dc0a83", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "score": 0.6208438873291016, + "metadata": { + "text": "accuracy of predictive networks [40, 5153]. We have also recently demonstrated\nhow this class of network can be used to inform associations identied in GW Astudies [40].\n9 Summary\nThe 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", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIGt6tExGqoQTRXd4fPWOb4MUvYWu", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The study of genetic pleiotropy has been advanced by data available on GeneNetwork.org through several key developments:\n\n1. **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].\n\n2. **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].\n\n3. **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].\n\nThese features collectively enhance the ability to study genetic pleiotropy by providing comprehensive data, advanced analytical tools, and a collaborative platform for researchers.", + "role": "assistant" + } + } + ], + "created": 1728918184, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 271, + "prompt_tokens": 1616, + "total_tokens": 1887 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b3bb8c8a-a222-5b62-94c5-54910d338fa7", + "score": 0.6981419684717742, + "metadata": { + "text": "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y\n921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; \n922 2010. pp. 767773. doi:10.1534/genetics.110.122549\n923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and \n924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. \n925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "title": "2022 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "version": "v0", + "chunk_order": 230, + "document_id": "4198ec53-60f1-55d1-8759-b9ede1d098c0", + "extraction_id": "2557b3fa-5aed-53f2-a4ca-afbed6154346", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "da910108-9a4b-5482-a4cb-bdb969cf959c", + "score": 0.6981419684717742, + "metadata": { + "text": "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y\n921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; \n922 2010. pp. 767773. doi:10.1534/genetics.110.122549\n923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and \n924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. \n925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "title": "2021 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "version": "v0", + "chunk_order": 230, + "document_id": "9ab8b190-fb4f-5bb0-8d04-1cd07a42192a", + "extraction_id": "6b791cd6-0d92-52fb-ac76-d3b0bb4ed535", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "29d6e248-c012-56f7-85c5-1ee104731db0", + "score": 0.6922983297946109, + "metadata": { + "text": "advances, the more examples become known which canbe explained only under the assumption of pleiotropy (Plate 1910, quoted from M cKusick 1976, pp. 301302).\nHis 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.\nDEVELOPMENT OF PLEIOTROPIC RESEARCH\nOne of the rst experimental studies of the mecha-", + "title": "2010 - One Hundred Years of Pleiotropy A Retrospective.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "c0995711-1389-52b7-a7a9-c92e5709fe43", + "extraction_id": "9b6ebb70-4cc0-5f53-bbbb-815ea191f2fa", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6919262409210205, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "score": 0.6817529201507568, + "metadata": { + "text": "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While\nthe candidate gene approach asks which one gene mutation causes a particular disease, the\nsystems genetics approach explores which phenotypes and diseases result from diverse sets\nof genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in\nGeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "6c9146cb-b00f-5f4c-8fc0-5a15a41405ec", + "score": 0.677685022354126, + "metadata": { + "text": "34. Pyeritz, R.E. (1989) Pleiotropy revisited: molecular explanations of a classic\nconcept. Am. J. Med. Genet. ,34, 124134.\n35. Gruneberg, H. (1938) An analysis of the pleiotropic effects of a lethal\nmutation in the rat. Proc. R. Soc. Lond. B. ,125, 123144.\n36. Wagner, G.P. and Zhang, J. (2011) The pleiotropic structure of the\ngenotypephenotype map: the evolvability of complex organisms. Nat.\nRev. Genet. ,12, 204213.\n37. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.", + "title": "2014 - Mendelian randomization genetic anchors for causal inference.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "05a32734-5dff-5430-b383-72a3d2e03792", + "extraction_id": "3ac0a087-d982-5d06-b351-d2f1e635c5b0", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "89a8170c-a7b5-5236-8ef3-7d0e6918e584", + "score": 0.6744906500285488, + "metadata": { + "text": "21. Byars, S. G. et al. Genetic loci associated with coronary artery disease harbor\nevidence of selection and antagonistic pleiotropy. PLoS Genet. 13, e1006328\n(2017).\n22. Rodrguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation\ninuence human senescence and disease. Nat. Ecol. Evol. 1, 0055 (2017).\n23. Institute for Health Metrics and Evaluation. Findings from the Global Burden\nof Disease Study 2017 (IHME, 2018).", + "title": "2020 - Multivariate genomic scan implicates novel loci.pdf", + "version": "v0", + "chunk_order": 133, + "document_id": "8529f0c6-a65b-53ed-9663-02d52dd82631", + "extraction_id": "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "12cdef3c-ff25-5349-8ef8-44f08065de4a", + "score": 0.6739315986633301, + "metadata": { + "text": "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\nin more length by Gerlai\n4and in Wang5in the context of RI strains.\nGeneNetwork\nGeneNetwork (GN, www.genenetwork.org ) is an open web resource that enables", + "title": "2018 - The Use of Recombinant Inbred Strains in Systems Genetics and Functional Analyses in Behavioral Pharmacology.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "337b2462-f1ec-530a-84de-97b13a0b9446", + "extraction_id": "3b23d583-7046-5dce-a506-fab0c2752977", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "a62e58c3-d1a6-54e9-809f-d98488089738", + "score": 0.6705227690623552, + "metadata": { + "text": "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704\n19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018;\n137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333\n20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla-\ntions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/\n10.1038 /ng.3406 PMID: 26414676", + "title": "2022 - System Genetics in the Rat Family.pdf", + "version": "v0", + "chunk_order": 223, + "document_id": "426b5aeb-1550-5039-8f2a-bd83d17c8648", + "extraction_id": "38cbdb87-820c-587e-9511-69d0ba74457a", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "6d34c5df-c9e5-5b22-b2af-2c1f191d984f", + "score": 0.6705227690623552, + "metadata": { + "text": "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704\n19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018;\n137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333\n20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla-\ntions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/\n10.1038 /ng.3406 PMID: 26414676", + "title": "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf", + "version": "v0", + "chunk_order": 223, + "document_id": "bac2ab98-4317-59ed-99ef-deda8c22786d", + "extraction_id": "2e135c0b-af2c-54fa-8661-aa4a3e31c0da", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIGtEMdN8awavmFIcxxBrdyWkpsf8", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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.", + "role": "assistant" + } + } + ], + "created": 1728918192, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 104, + "prompt_tokens": 1300, + "total_tokens": 1404 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "496d27de-6dd0-5f6a-bedb-64d4c252981d", + "score": 0.7664211392402703, + "metadata": { + "text": "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 \net \n \nal \n. (2007) used GenAge to construct a modular\nnetwork 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 \net \n \nal", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 37, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "df726361-271a-5dbb-b6d1-03dab5a63006", + "score": 0.7302705210286081, + "metadata": { + "text": "[111], and for generation of networks based on known gene \ninteractions 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-\ncation by regression (NIR) [119] has been used to identify", + "title": "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "extraction_id": "53c57cc4-4d43-505a-974c-442d06e144df", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "300065ff-2ddb-532e-ab5d-a9b0903c8d21", + "score": 0.7285561723377615, + "metadata": { + "text": "network analysis is a useful approach toward identifying genetic \ndeterminants of longevity . PLoS One , 2008 , 3(11), e3802. \n[38] Bell, R.; Hubbard, A.; Che ttier, R.; Chen, D.; Miller, J.P.; Kapahi, \nP.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A \nhuman protein interaction network shows conservation of aging \nprocesses between human and invertebrate species . PLoS Genet , \n2009 , 5(3), e1000414. \n[39] Budovsky, A.; Abramovich, A.; Cohen, R.; Chalifa-Caspi, V.;", + "title": "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "extraction_id": "e26cef53-9a67-508e-8a29-2f40a6aa45b0", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "score": 0.7223046587524035, + "metadata": { + "text": "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", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "15f6d690-61b1-5de3-ac40-10e46777afa8", + "score": 0.7212184403874349, + "metadata": { + "text": "30. Vartiainen, S., Aarnio, V., Lakso, M. & Wong, G. Increased lifespan in\ntransgenic Caenorhabditis elegans overexpressing human -synuclein. Exp.\nGerontol. 41, 871 876 (2006).\n31. Lpez-Otn, C. et al. The hallmarks of aging. Cell153, 1194 1217 (2013).\n32. Kenyon, C. J. The genetics of ageing. Nature 464, 504 512 (2010).\n33. Liberzon, A. et al. The molecular signatures database hallmark gene set\ncollection. Cell Syst. 1, 417 425 (2015).", + "title": "2020 - Multivariate genomic scan implicates novel loci.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "8529f0c6-a65b-53ed-9663-02d52dd82631", + "extraction_id": "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "9f662099-6f46-5af7-a6c1-4d0945b9a931", + "score": 0.71908362394073, + "metadata": { + "text": "1118 compared to young ones. Overall, our results revealed that six pathways and six key genes might play pivotal roles \nin 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.\nKeywords Drosophila melanogaster Longevity Gene Pathway Network\nIntroduction", + "title": "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "extraction_id": "4109e561-4721-5f4e-b4d5-4353f8d1741d", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "c96b67f8-ad31-50fd-b053-07b127938ef2", + "score": 0.708771978763106, + "metadata": { + "text": "During the past century, remarkable progress has been \nmade 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", + "title": "2020 - Mitonuclear genomics and aging.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "extraction_id": "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "score": 0.7055839826612516, + "metadata": { + "text": "In addition to aging- and CR-related genes, another\nsource 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.\nMany longevity-associated genes are related to spe-", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "d59d7882-333d-5576-86ab-3cfa6354b946", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "a05a46db-5443-566c-9494-212f86ee2eb3", + "score": 0.7047041234550415, + "metadata": { + "text": "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.\nI. Introduction", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "df213743-7428-59be-ba19-2563f8ce5c70", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "016ee489-a313-5648-803d-db50217ae084", + "score": 0.7021947858101385, + "metadata": { + "text": "Network approaches are instrumental in discerning \nglobal properties of aging/lifespan regulators, making com-\nputational 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 \nthe literature, the lack of spatial and temporal information, or \nthe limited coverage of the network [44]. \n4. EPIGENETIC REGULATION OF AGING \n In addition to gene expression changes, the states of epi-", + "title": "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "extraction_id": "a74345ec-ceee-5290-990b-ea338e735937", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIGtIvgudl04cUWtfjaShHQ8PZDZI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "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].", + "role": "assistant" + } + } + ], + "created": 1728918196, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 180, + "prompt_tokens": 1247, + "total_tokens": 1427 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a374d88e-458e-5252-8b3a-5ca162fa6982", + "score": 0.7924221877800128, + "metadata": { + "text": "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk\nanalysis using the sum of the weight of each risk-associated locus of genomic sequence\nobtained from the corresponding evidence. These weights are assessed from the regression\ncoefcient associated with each locus. These combined genetics features and correlation\nmatrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "3c30b33b-8928-5cee-9c37-c70642fff75c", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "score": 0.7441587753106677, + "metadata": { + "text": "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. \nA 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 \ngenetic variance underlying a trait, the overall effect size", + "title": "2021 - Genetics and genomics of arrhythmic.pdf", + "version": "v0", + "chunk_order": 81, + "document_id": "462ed035-e4fb-5847-a92d-927f05a2b58b", + "extraction_id": "ada410d0-6b91-5959-b834-cc3389e29c5f", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "f36bf430-26bd-5031-a392-14f3c43367ab", + "score": 0.7282247109279222, + "metadata": { + "text": "of genome-wide genotypes and publicly available data from large consortia, GRSs with a larger number of vari-\nants are being used, and the predictive value of these genome-wide polygenic risk scores (PRSs) has substantially improved\n50,51.\nPRSs can be derived using different approaches, however, these require both summary statistics from an exter -", + "title": "2020 - Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "494779f3-1437-5b50-a9b2-3f616a048719", + "extraction_id": "8292e291-87bb-5f04-8e40-fb2228da3927", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "4190e1d8-ae9e-5c42-8842-aa0a60a2bb2c", + "score": 0.7133836150169373, + "metadata": { + "text": "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-\ndividuals with similar ancestry, whereas limited bene ti s", + "title": "2023 - Clinical, technical, and environmental biases.pdf", + "version": "v0", + "chunk_order": 71, + "document_id": "6a81e435-bd17-558d-850a-44ee3dbab5bd", + "extraction_id": "50731787-cf17-5284-b3f4-2c551cb41c90", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "score": 0.7085323544899335, + "metadata": { + "text": "(GWAS) in diverse populations have identified hundreds \nof genetic loci associated with T2D [79]. Polygenic risk \nscores (PRS), which aggregate the genetic risk of individ -\nual alleles across the genome, are thus promising to pre -\ndict future T2D occurrence and improve early diagnosis, \nintervention, and prevention of T2D [1015]. However, \nto date, T2D PRS were most widely developed and vali -\ndated in individuals of European descent. Given that the \npredictive performance of PRS often attenuates in non-", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "17c49e58-c89a-5495-b17f-adcade90a4c6", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "score": 0.7070292830467224, + "metadata": { + "text": "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter \nvention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most \nwidely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non\nEuropean populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "459f7eed-490a-5586-9d2a-20f721daa6bc", + "score": 0.701218843460083, + "metadata": { + "text": "Letters NATure GeNeTicsMethods\nPolygenic score derivation. Polygenic scores provide a quantitative metric of \nan 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.", + "title": "2018 - Genome-wide polygenic scores for common diseases.pdf", + "version": "v0", + "chunk_order": 73, + "document_id": "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "extraction_id": "0a80e61e-648a-5122-9b17-8177bc734674", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "98da512f-fee2-501b-b093-9ee7ab22c5f9", + "score": 0.6958812475204468, + "metadata": { + "text": "(Fig. 1B ). Polygenic risk scores (PRS) have emerged as promising\nbiomarkers for the prediction of disease risk, not only in the area of\ncardiovascular disorders, but also oncology (21). These risk scores also\nhave become increasingly available for a multitude of phenotypes and\nare systematically curated in a free online database (22).\nIt has been shown that certain preexisting autoimmune diseases as\nwell as the occurrence of imAE upon treatment are associated with", + "title": "2022 - Coming of Age Human Genomics.pdf", + "version": "v0", + "chunk_order": 28, + "document_id": "45506895-eef1-57f4-8ca1-79fe23a2493f", + "extraction_id": "ca2e1560-db8f-5c3f-b7bf-dd1beaa94655", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "d27fbbe8-aec0-510f-ab9d-1a0d4f0a1678", + "score": 0.6924620866775513, + "metadata": { + "text": "eases identify individuals with risk equivalent to monogenicmutations. Nat. Genet. ,50, 12191224.\n13. Euesden, J., Lewis, C.M. and OReilly, P.F. (2015) PRSice: poly-\ngenic risk score software. Bioinformatics ,31, 14661468.\n14. Belsky, D.W., Moffitt, T.E., Sugden, K., Williams, B., Houts, R.,\nMcCarthy, J. and Caspi, A. (2013) Development and evalu-\nation of a genetic risk score for obesity.\nBiodemography Soc.\nBiol.,59, 85100.\n15. De Jager, P.L., Chibnik, L.B., Cui, J., Reischl, J., Lehr, S., Simon,", + "title": "2020 - Genome-wide assessment of genetic risk for systemic.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "af34f0df-a726-5cc4-844f-a5d67273d9a0", + "extraction_id": "9b1cee76-2c59-50d6-a37c-8c593336fe33", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "b3e446bb-e438-5d66-a34c-8e1de0ebb639", + "score": 0.6883896589279175, + "metadata": { + "text": "in tissue-specic regions or use gene co-expression information\nmay provide a more comprehensive view of a specic gene or\na gene networks role in modulating an individuals response\nto environmental variations, compared to that provided by\nthe single candidate gene approach (Gamazon et al., 2015;\nBarth et al., 2020). Expression-based polygenic risk scores\n(ePRS) oer one such approach to understand the underlying\ngenetic background linked to behavioral outcomes (Hari Dass", + "title": "2021 -Potter-Dickey- Genetic Susceptibility.pdf", + "version": "v0", + "chunk_order": 32, + "document_id": "cb119609-daa3-56af-97ff-b809cc39c210", + "extraction_id": "567a2f7e-0ff9-5229-bfeb-066b6e6f50f6", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + } + ], + "kg_search_results": null + } + } + } +} \ No newline at end of file -- cgit v1.2.3