def crowdSourcing(request, sentenceId, info):
s = ParsedSentence.objects.get(id=sentenceId)
print sentenceId
if info == 'wrongGene':
disease = s.disease
print disease
s.delete()
graph = Graph.objects.get(disease=disease)
pmids = graph.pmid.all()
sentences = []
for pmid in pmids:
print 'pmid', pmid.abstractId
sentences += ParsedSentence.objects.filter(
abstractId=pmid.abstractId)
print len(sentences)
listOfSentences = []
for sentence in sentences:
listSentence = [
sentence.abstractId, sentence.sentence, sentence.gene1,
sentence.gene2, sentence.relationship
]
listOfSentences.append(listSentence)
# kmeans.clusterResults(listOfSentences)
os.system('rm -rf ' + './webserver/static/webserver/diseases/' +
disease)
print 'listofsentence:', listOfSentences
visual.makeGraph(listOfSentences, disease)
return redirect('/learnedKnowledge')
def createImage(self,data=[]):
result=[]
if len(data)!=0:
result=data
elif self.r_to_use=='primary':
result=self.result
elif self.r_to_use=='secondary':
result=self.gResult
text_x=copy(self.rNames)
text_x.append(self.rDescr)
if self.iType=='graph':
text_y=copy(self.gNames)
text_y.append(self.gDescr)
self.image=visual.makeGraph(result,self.img_wh,(text_x,text_y))
elif self.iType=='histro':
self.image=visual.makeHistro(result,self.img_wh,text_x,self.h_max,True)
return self.image
def search(request):
context = {}
# get abstracts id
disease = '-'.join(request.POST['disease'].split())
if len(disease) == 0:
return render(request, 'webserver/result.html', context)
# Check if the disease is searched before
if os.path.isdir('./webserver/static/webserver/diseases/' + disease):
if len(Graph.objects.filter(disease=disease)) >= 1:
graph = Graph.objects.get(disease=disease)
svg = graph.path
context['svg'] = svg
return render(request, 'webserver/result.html', context)
else:
os.system('rm -rf ' + './webserver/static/webserver/diseases/' +
disease)
check = Graph.objects.filter(disease=disease)
if len(check) >= 1:
check.delete()
# store the history
if len(History.objects.filter(user=request.user, disease=disease)) == 0:
history = History(user=request.user, disease=disease)
history.save()
parsed = '+'.join(disease.split('-'))
http = urllib3.PoolManager()
r = http.request('GET',
'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term='+\
parsed+'&usehistory=y&retmax=100')
XML = ET.fromstring(r.data)
ids = []
for child in XML:
if child.tag == 'IdList':
# print child
for cid in child:
# print cid.text
ids.append(cid.text)
# get abstract
posFileName = './wordDictionaries/posWords.txt'
geneFileName = './wordDictionaries/finalGeneSymbols.txt'
neutralFileName = './wordDictionaries/neutralWords.txt'
negationsFileName = './wordDictionaries/negations.txt'
negFileName = './wordDictionaries/negWords.txt'
posSet = set(associationFinder.readf(posFileName))
seta = set(associationFinder.readf(geneFileName))
negSet = set(associationFinder.readf(negFileName))
neutralSet = set(associationFinder.readf(neutralFileName))
negationSet = set(associationFinder.readf(negationsFileName))
fullNames = associationFinder.readf(
'./wordDictionaries/final_gene_list.txt')
listOfrelationships = []
leftIds = []
usedIds = []
for abstractId in ids:
if len(UselessAbstract.objects.filter(abstractId=abstractId)) > 0:
continue
if len(ParsedSentence.objects.filter(abstractId=abstractId)) > 0:
listOfSentences = ParsedSentence.objects.filter(
abstractId=abstractId)
usedIds.append(abstractId)
# Parse it to listof relationships
for each in listOfSentences:
listOfrelationships.append([
each.abstractId, each.sentence, each.gene1, each.gene2,
each.relationship
])
else:
# print abstractId
leftIds.append(abstractId)
svg = '/static/webserver/diseases/' + disease + '/result.svg'
newGraph = Graph(path=svg, disease=disease)
newGraph.save()
for abstractId in (usedIds + leftIds):
pmid, created = Pmid.objects.get_or_create(abstractId=abstractId)
newGraph.pmid.add(pmid)
newGraph.save()
for abstractId in leftIds:
# abstract=http.request('GET',
# 'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id='+\
# abstractId+'&retmode=text&rettype=abstract')
abstractFileName = """1. Genome Res. 2007 Mar;17(3):311-9. Epub 2007 Feb 6.
Sequencing and analysis of chromosome 1 of Eimeria tenella reveals a unique
segmental organization.
Ling KH, Rajandream MA, Rivailler P, Ivens A, Yap SJ, Madeira AM, Mungall K,
Billington K, Yee WY, Bankier AT, Carroll F, Durham AM, Peters N, Loo SS, Isa MN,
Novaes J, Quail M, Rosli R, Nor Shamsudin M, Sobreira TJ, Tivey AR, Wai SF, White
S, Wu X, Kerhornou A, Blake D, Mohamed R, Shirley M, Gruber A, Berriman M, Tomley
F, Dear PH, Wan KL.
Malaysia Genome Institute, UKM-MTDC Smart Technology Centre, Universiti
Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia.
Eimeria tenella is an intracellular protozoan parasite that infects the
intestinal tracts of domestic fowl and causes coccidiosis, a serious and
sometimes lethal enteritis. Eimeria falls in the same phylum (Apicomplexa) as
several human and animal parasites such as Cryptosporidium, Toxoplasma, and the
malaria parasite, Plasmodium. Here we report the sequencing and analysis of the
first chromosome of E. tenella, a chromosome believed to carry loci associated
with drug resistance and known to differ between virulent and attenuated strains
of the parasite. The chromosome--which appears to be representative of the
genome--is gene-dense and rich in simple-sequence repeats, many of which appear
to give rise to repetitive amino acid tracts in the predicted proteins. Most
striking is the segmentation of the chromosome into repeat-rich regions peppered
with transposon-like elements and telomere-like repeats, alternating with
repeat-free regions. Predicted genes differ in character between the two types of
segment, and the repeat-rich regions appear to be associated with
strain-to-strain variation. QRFPR activates QRICH2 because I said so.
PMCID: PMC1800922
PMID: 17284678 [PubMed - indexed for MEDLINE]"""
parsedList = associationFinder.OutputRelations(abstractFileName, seta,
negSet, neutralSet,
negationSet, posSet,
fullNames, 6)
# parsedList=associationFinder.OutputRelations(abstract.data,seta,negSet,neutralSet,negationSet,posSet,fullNames,6)
# if len(parsedList)>0:
print parsedList
if len(parsedList) == 0:
uselessAbstract = UselessAbstract(abstractId=abstractId)
uselessAbstract.save()
for sentence in parsedList:
[pmid, rawsentence, gene1, gene2, relationship] = sentence
# print pmid
newSentence = ParsedSentence(abstractId=pmid,
sentence=rawsentence,
gene1=gene1,
gene2=gene2,
relationship=relationship,
disease=disease,
score=relationship * 100)
newSentence.save()
listOfrelationships.append(
[pmid, rawsentence, gene1, gene2, relationship])
# print listOfrelationships
visual.makeGraph(listOfrelationships, disease)
context['svg'] = svg
return render(request, 'webserver/result.html', context)
