def get(self, name):
CHIP_result = pd.DataFrame(columns=[
'src_entrez', 'trg_entrez', 'srcname', 'trgname', 'find_pmid',
'all_pmids', 'mode', 'score', 'evi_pmid', 'evi_sent', 'report'
])
#query_genes=['AATF','BAX'] #Symbol MUST [TF,Target]
req_ids = name.split(";")
#query_id=[26574,581]#NCBI ID MUST [TF,Target]
query_id = []
mesh = 'humans' # default value
try:
query_id.append(int(req_ids[0]))
query_id.append(int(req_ids[1]))
if (len(req_ids) == 3):
mesh = req_ids[2]
except:
return "Requested string is not correct (TF_EntrezID;Target_EntrezID;MeSH_term)! Template: 26574;581;humans", 404
try:
query_genes = fn.retrieve_symbol(query_id)
query_genes, query_id, single_gene, single_id = fn.make_query(
genes, lookup_ids, query_genes, query_id)
except:
return "No annotation has been retrieved associated with input Entrez IDs! Enter correct Entrez IDs...", 404
try:
myterm = fn.term_maker(single_gene, genes, mesh)
#### ESearch: Searching the Entrez databases
Entrez.email = "*****@*****.**"
handle = Entrez.esearch(db="pubmed", term=myterm, retmax=100000000)
record = Entrez.read(handle)
PubIDs = record["IdList"]
except:
return "NCBI server request error", 404
if (len(PubIDs) > 0):
sum_ranks = []
evi_pmids = []
evi_sentence = []
all_pmids = ';'.join(PubIDs)
for PubID in PubIDs:
abstract = ''
ranks = []
annot_df = pd.DataFrame(
columns=['type', 'id', 'text', 'offset', 'length'])
try:
annot_df, abstract = fn.pubtator_annot(annot_df, PubID)
except:
abstract = fn.ret_abstract(PubID)
if (abstract == '?'):
status += "PMID=[" + PubID + "] does not exist any more|||"
continue # remove it from the output results in TRRUST
else:
status += "PMID=[" + PubID + "] PubTator Response is not readable, Try to annotate manually|||"
#print(status)
# try:
# beCAS_lookup_full=fn.beCAS_lookup(PubID,query_id)
# beCAS_lookup=beCAS_lookup_full[['type','id','text','offset','length']]
# annot_df=pd.concat([annot_df,beCAS_lookup], ignore_index=True)
# except:
# status+="beCAS Server error|||"
lookup_results = fn.lookup_annot(abstract, query_genes,
query_id, lookup_ids)
annot_df = annot_df.append(lookup_results)
# surface_annot=fn.surface_similarity(abstract, genes, query_genes, query_id,lookup_ids,single_id)
# annot_df=annot_df.append(surface_annot)
annot_df = annot_df.drop_duplicates(subset=['id', 'offset'])
annot_df = fn.multiple_taxonomy(annot_df, query_id)
annot_df = annot_df.reset_index(drop=True)
candidate_sentences, covered = fn.candidate_sentence(
annot_df, abstract, query_id)
if (len(candidate_sentences.index) == 0):
status += "PMID=[" + PubID + "] No co-existed sentences found in the abstract|||"
#print(status)
continue
for sentence in candidate_sentences.itertuples():
obj = Rules(Positive, Negative, annot_df, covered,
abstract, query_genes, query_id, sentence)
depgraphs = fn.dep_parser('8000', sentence, annot_df,
query_id, single_id, Positive,
Negative, 2)
if (depgraphs):
try:
obj.multiplication_score(depgraphs, single_id)
except:
status += "PMID=[" + PubID + "] dependency graph score error|||"
else:
status += "PMID=[" + PubID + "] dependency graph co-occurance of single ids error|||"
#obj.search_ranking()
ranks.append(obj.rank)
if (obj.rank != 0):
evi_sentence.append('[' + PubID + ']' +
sentence.sentence)
evi_pmids.append(PubID)
if (len(ranks) != 0):
sum_ranks.append(sum(ranks))
mode = ''
rank_T = sum(sum_ranks)
if (rank_T > 0):
mode = 'positive'
if (rank_T < 0):
mode = 'negative'
evi_sentence = '|||'.join(evi_sentence)
evi_pmids = ';'.join(evi_pmids)
CHIP_result = CHIP_result.append(
{
'src_entrez': single_id[0],
'trg_entrez': single_id[1],
'srcname': single_gene[0],
'trgname': single_gene[1],
'find_pmid': str(len(all_pmids)),
'all_pmids': all_pmids,
'mode': mode,
'score': str(rank_T),
'evi_pmid': evi_pmids,
'evi_sent': evi_sentence,
'report': status
},
ignore_index=True)
result = [{
"TF": CHIP_result["srcname"].tolist()[0],
"Target_gene": CHIP_result["trgname"].tolist()[0],
"Sign": CHIP_result["mode"].tolist()[0],
"Score": CHIP_result["score"].tolist()[0],
"PMID_evidence": CHIP_result["evi_pmid"].tolist()[0],
"Sentence_evidence": CHIP_result["evi_sent"].tolist()[0]
}]
return result, 200
else:
return "Not found any PMIDs for the input interaction", 404
# beCAS_lookup=beCAS_lookup_full[['type','id','text','offset','length']]
# annot_df=pd.concat([annot_df,beCAS_lookup], ignore_index=True)
# except:
# status+="beCAS Server error|||"
lookup_results = fn.lookup_annot(abstract, query_genes, query_id,
lookup_ids)
annot_df = annot_df.append(lookup_results)
# surface_annot=fn.surface_similarity(abstract, genes, query_genes, query_id,lookup_ids,single_id)
# annot_df=annot_df.append(surface_annot)
annot_df = annot_df.drop_duplicates(subset=['id', 'offset'])
annot_df = fn.multiple_taxonomy(annot_df, query_id)
annot_df = annot_df.reset_index(drop=True)
candidate_sentences, covered = fn.candidate_sentence(
annot_df, abstract, query_id)
if (len(candidate_sentences.index) == 0):
status += "PMID=[" + PubID + "] No co-existed sentences found in the abstract|||"
#print(status)
continue
for sentence in candidate_sentences.itertuples():
obj = Rules(Positive, Negative, annot_df, covered, abstract,
query_genes, query_id, sentence)
depgraphs = fn.dep_parser('9000', sentence, annot_df, query_id,
single_id, Positive, Negative, 2)
if (depgraphs):
try:
obj.multiplication_score(depgraphs, single_id)
except:
status += "PMID=[" + PubID + "] dependency graph score error|||"
else: