1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
|
#!/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.split("|"):
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):
# 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 + "'} },\n"
return(json0)
def generate_edges(data):
json0=str()
for line in data.split("\n"):
if len(line.strip())!=0:
(source, cat, target, pmid, sent) = line.split("\t")
edgeID=source+"_"+target
json0+="{ data: { id: \'" + edgeID + "\', source: \'" + source + "\', target: '" + target + "\' } },\n"
return(json0)
addiction_d = {"reward":"reward|reinforcement|conditioned place preference|CPP|self-administration|self-administered",
"aversion":"aversion|aversive|conditioned taste aversion|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)
#out1=gene_anatomical(gene)
#out2=gene_functional(gene)
#report=out0+out1+out2
#with codecs.open(gene+"_addiction_sentences.tab", "w", encoding='utf8') as writer:
# writer.write(report)
# writer.close()
n0=generate_nodes(function_d)
n1=generate_nodes(addiction_d)
n2=generate_nodes(drug_d)
n3=generate_nodes(brain_d)
default_nodes=n0+n1+n2+n3
|
