rename to ratspub

1 files changed, 0 insertions, 136 deletions

diff --git a/gatpub.py b/gatpub.py
deleted file mode 100755
index c853fd2..0000000
--- a/gatpub.py
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/bin/env python3 
-from nltk.tokenize import sent_tokenize
-import os
-import re
-import codecs
-import sys
-
-#gene=sys.argv[1]
-
-## turn dictionary (synonyms) to regular expression
-def undic(dic):
-    return "|".join(dic.values())
-
-def findWholeWord(w):
-    return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search
-
-def getSentences(query, gene):
-    abstracts = os.popen("esearch -db pubmed -query " +  query + " | efetch -format uid |fetch-pubmed -path /run/media/hao/PubMed/Archive/ | xtract -pattern PubmedArticle -element MedlineCitation/PMID,ArticleTitle,AbstractText").read()
-    out=str()
-    for row in abstracts.split("\n"):
-        tiab=row.split("\t")
-        pmid = tiab.pop(0)
-        tiab= " ".join(tiab)
-        sentences = sent_tokenize(tiab)
-        ## keep the sentence only if it contains the gene 
-        for sent in sentences:
-            if findWholeWord(gene)(sent):
-                sent=re.sub(r'\b(%s)\b' % gene, r'<b>\1</b>', sent, flags=re.I)
-                out+=pmid+"\t"+sent+"<br>\n"
-    return(out)
-
-def gene_addiction(gene):
-    # search gene name & drug name  in the context of addiction terms (i.e., exclude etoh affects cancer, or methods to extract cocaine) 
-    q="\"(" + addiction.replace("|", " OR ")  + ") AND (" + drug.replace("|", " OR ", ) + ") AND " + gene + "\""
-    sents=getSentences(q, gene)
-    out=str()
-    for sent in sents.split("\n"):
-        for drug0 in drug_d:
-            if findWholeWord(drug_d[drug0])(sent) :
-                sent=re.sub(r'\b(%s)\b' % drug_d[drug0], r'<b>\1</b>', sent, flags=re.I)
-                out+=gene+"\t"+"drug\t" + drug0+"\t"+sent+"\n"
-        for add0 in addiction_d:
-            if findWholeWord(add0)(sent) :
-                sent=re.sub(r'\b(%s)\b' % add0, r'<b>\1</b>', sent, flags=re.I)
-                out+=gene+"\t"+"addiction\t"+add0+"\t"+sent+"\n"
-    return(out)
-
-def gene_anatomical(gene):
-    q="\"(" + brain.replace("|", " OR ")  + ") AND " + gene + "\""
-    sents=getSentences(q,gene)
-    out=str()
-    for sent in sents.split("\n"):
-        for brain0 in brain_d:
-            if findWholeWord(brain_d[brain0])(sent) :
-                sent=re.sub(r'\b(%s)\b' % brain_d[brain0], r'<b>\1</b>', sent, flags=re.I)
-                out+=gene+"\t"+"brain\t"+brain0+"\t"+sent+"\n"
-    return(out)
-
-def gene_functional(gene):
-    q="\"(" + function.replace("|", " OR ")  + ") AND " + gene + "\""
-    sents=getSentences(q,gene)
-    out=str()
-    for sent in sents.split("\n"):
-        for bio0 in function_d:
-            if findWholeWord(function_d[bio0])(sent) :
-                sent=re.sub(r'\b(%s)\b' % function_d[bio0], r'<b>\1</b>', sent, flags=re.I)
-                out+=gene+"\t"+"function\t"+bio0+"\t"+sent+"\n"
-    return(out)
-
-def generate_nodes(nodes_d, nodecolor):
-    # include all search terms even if there are no edges, just to show negative result 
-    json0 =str() #"{ data: { id: '" + gene +  "'} },\n"
-    for node in nodes_d:
-        json0 += "{ data: { id: '" + node +  "', nodecolor: '" + nodecolor + "' } },\n"
-    return(json0)
-
-def generate_edges(data):
-    json0=str()
-    edgeCnts={}
-    for line in  data.split("\n"):
-        if len(line.strip())!=0:
-            (source, cat, target, pmid, sent) = line.split("\t")
-            edgeID=source+"|"+target
-            if edgeID in edgeCnts:
-                edgeCnts[edgeID]+=1
-            else:
-                edgeCnts[edgeID]=1
-    for edgeID in edgeCnts:
-        (source,target)=edgeID.split("|")
-        json0+="{ data: { id: \'" + edgeID + "\', source: \'" + source + "\', target: '" + target + "\', sentCnt: '" + str(edgeCnts[edgeID]) + "' } },\n"
-    return(json0)
-
-
-
-addiction_d = {"reward":"reward|reinforcement|conditioned place preference|CPP|self-administration|self-administered",
-        "aversion":"aversion|aversive|CTA|withdrawal",
-        "relapse":"relapse|reinstatement|craving|drug seeking",
-        "sensitization":"sensitization",
-        "addiction":"addiction|drug abuse",
-        "intoxication":"intoxication|binge"
-        }
-addiction=undic(addiction_d)
-
-drug_d = {"alcohol":"alcohol|alcoholism",
-        "nicotine":"smoking|nicotine|tobacco",
-        "amphetamine":"methamphetamine|amphetamine|METH",
-        "cocaine":"cocaine",
-        "opioid":"opioid|fentanyl|oxycodone|oxycontin|heroin|morphine",
-        "cannabinoid":"marijuana|cannabinoid|tetrahydrocannabinol|thc|thc-9"
-        }
-drug=undic(drug_d)
-
-brain_d ={"cortex":"cortex|pfc|vmpfc|il|pl|prelimbic|infralimbic",
-          "striatum":"striatum|STR",
-          "accumbens":"shell|core|NAcc|acbs|acbc",
-          "hippocampus":"hippocampus|hipp|hip|ca1|ca3|dentate|gyrus",
-          "amygadala":"amygadala|cea|bla|amy",
-          "vta":"ventral tegmental|vta|pvta"
-          }
-# brain region has too many short acronyms to just use the undic function, so search PubMed using the following 
-brain="cortex|accumbens|striatum|amygadala|hippocampus|tegmental|mesolimbic|infralimbic|prelimbic"
-
-function_d={"plasticity":"LTP|LTD|plasticity|synaptic|epsp|epsc",
-            "neurotransmission": "neurotransmission|glutamate|GABA|cholinergic|serotoninergic",
-            "signalling":"signalling|phosphorylation|glycosylation",
-#            "regulation":"increased|decreased|regulated|inhibited|stimulated",
-            "transcription":"transcription|methylation|histone|ribosome",
-            }
-function=undic(function_d)
-
-#https://htmlcolorcodes.com/
-n0=generate_nodes(function_d, "#D7BDE2")
-n1=generate_nodes(addiction_d,"#A9CCE3")
-n2=generate_nodes(drug_d, "#A3E4D7")
-n3=generate_nodes(brain_d, "#F9E79F")
-default_nodes=n0+n1+n2+n3