def fetch_abstract(pmids):
pmid_str = ",".join(pmids)
try:
handle = efetch(db='pubmed', id=pmid_str, retmode='xml')
except urllib.error.HTTPError:
handle = efetch(db='pubmed', id=pmid_str, retmode='xml')
xml_data = read(handle)['PubmedArticle']
try:
articles = [rec['MedlineCitation'] for rec in xml_data]
except KeyError:
articles = None
return articles
def author(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)['PubmedArticle'][0]
data = xml_data['MedlineCitation']['Article']['AuthorList']
author_list = []
for n in range(len(data)):
author = data[n]
name = author['ForeName'] + ' ' + author['LastName']
aff_info = author['AffiliationInfo']
if aff_info:
aff = aff_info[0]['Affiliation']
else:
aff = ''
author_list.append('#Name ' + name)
if aff:
author_list.append('#Affiliation ' + aff)
author_str = ' '.join(author_list)
return author_str
def get_terms_abstract(self, pubmed_id):
handle = efetch(db='pubmed', id=pubmed_id, retmode='text', rettype='abstract', email='*****@*****.**')
text = handle.read()
terms = TermsBag()
# terms.termine_service(text)
self.view.terms[self.view.pubmed_id.isin([int(pubmed_id)])] = terms.termine_service(text)
self.terms = terms.to_data_frame()
def fetch_abstract(pmid):
##
## Return abstract of a given
## article using pmid
##
## => Return None when pmid can't be return
## (can happen when article is in chinese)
##
try:
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
xml_data = read(handle)
xml_data = xml_data['PubmedArticle'][0]
except:
return None
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except IndexError:
return None
except KeyError:
return None
except:
return None
def fetch_abstract(id_list):
ids = ','.join(id_list)
handle = efetch(db='pubmed', id=ids,
retmode='xml') ### this step returns xml object
#results = Entrez.read(handle) #this step read xml to string
#return results
return handle
def pubmed(gi,ids,query):
"""
Get the pubmed articles listed by *ids
"""
_ids=",".join(ids)
for id in ids:
handle = efetch(db="pubmed",id=id,retmode='xml',rettype='xml',retmax=MAX_RETURN)
try:
#print handle.read()
results = eread(handle)
for citation in results:
#runtime().debug(citation.keys())
citation = citation['MedlineCitation']
pmid = citation['PMID']
article = citation['Article']
title = article['ArticleTitle']
journal = article['Journal']['Title']
try:
date = citation['DateCompleted'] if citation.has_key('DateCompleted') else citation['DateCreated']
year = date['Year']
month = date['Month']
day = date['Day']
datetime = "%s-%s-%s" % (year,month,day)
except:
datetime = '0000-00-00'
runtime().debug("Parsed pmid:%s" % id)
yield Citation(gi, pmid, title, journal, datetime, query)
except:
runtime().debug("Failure fetching pmid:%s" % id)
continue
finally:
handle.close()
def fetch_abstract(pmid):
print(pmid)
Entrez.email = '*****@*****.**'
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)
return xml_data
def specialization(author,affiliation):
# import libraries
import wikipedia
import re
from Bio.Entrez import efetch, read
author = '"'+author+'"'
# Find ID's for doctor + affiliation
ids = []
results = search('%s[AUTH] AND %s[AFFL]' % (author,affiliation))['IdList']
for i in results:
ids.append(i)
num_paper = len(ids)
# get abstracts from list of ID's
query_abstracts = ''
keywords = []
query_keywords = ''
query_title = ''
for i in ids:
xml_data = read(efetch(db='pubmed', id=i, retmode='xml'))
try:
abstract = xml_data['PubmedArticle'][0]['MedlineCitation']['Article']['Abstract']['AbstractText']
query_abstracts = query_abstracts + str(abstract) + ' '
except:
print('Paper with ID: ' + i + ' has no abstract')
#get keuywords from ID's
if xml_data['PubmedArticle'][0]['MedlineCitation']['KeywordList'] != []:
for x in xml_data['PubmedArticle'][0]['MedlineCitation']['KeywordList'][0] :
keywords.append(str(re.sub("[^a-zA-Z]", " ", x)))
query_keywords = query_keywords + x + ' '
#get paper titel from ID's
try:
query_title = query_title + ' ' + xml_data['PubmedArticle'][0]['MedlineCitation']['Article']['ArticleTitle']
except:
print('Paper with ID: ' + i + ' has no title')
# get wiki pages first sentence of keywords
query_wiki = ''
for keyword in keywords:
try:
page = wikipedia.summary(keyword,sentences = 1)
query_wiki = query_wiki + ' ' + str(re.sub("[^a-zA-Z]", " ", page))
except:
print('Disambiguation error for keyword: '+keyword+', action: keyword excluded')
# find specialism
corpus = query_abstracts + ' ' + query_keywords + ' ' + query_wiki + ' ' + query_title
specialization = str(spec_search(corpus))
if num_paper == 0:
print('no papers found')
specialization = []
else:
print('this doctor is specialized in: '+specialization)
return specialization
def query(self, term):
id_list = self.__term_querier.query_matching_paper_ids(term)
id_list_string = ','.join(id_list)
handle = efetch(db=self.__db,
id=id_list_string,
retmode=self.__retmode)
papers = self.__entrez.read(handle)
return papers['PubmedArticle']
def get_abstract(pmid):
"""
function to collect abstract from pubmed
shameless taken from: https://stackoverflow.com/questions/17409107/obtaining-data-from-pubmed-using-python
"""
handle = efetch(db='pubmed', id=pmid, retmode='text', rettype='abstract')
return handle.read()
def print_xml(pmid):
"""
from the result of the query (pubmed ids) to parse out title, abstract, article type, and journal info
:param pmid: list of PubMed IDs after querying
:return: list of titles,abstracts, types, and journal names
"""
handle = efetch(db='pubmed',
id=','.join(map(str, pmid)),
retmode='xml',
rettype='text')
# try:
print('entering print_xml')
doc = handle.read()
# except http.client.IncompleteRead:
# continue
doc = xmltodict.parse(doc)
doc = json.loads(json.dumps(doc))
print('have read the doc')
d = doc['PubmedArticleSet']["PubmedArticle"]
titles = []
types = []
abstracts = []
jour_names = []
for i in d: # iterate through each article
# find journal information
if i["MedlineCitation"]['Article']['Journal']['Title'] is not None:
jour_name = i["MedlineCitation"]['Article']['Journal']['Title']
jour_names.append(jour_name)
else:
jour_names.append('no journal found')
# find title information
t = i["MedlineCitation"]['Article']['ArticleTitle']
if isinstance(t, str):
t = i["MedlineCitation"]['Article']['ArticleTitle']
elif i["MedlineCitation"]['Article']['ArticleTitle'] is None:
t = "no title"
else:
t = i["MedlineCitation"]['Article']['ArticleTitle']['#text']
titles.append(t)
if 'Abstract' in i['MedlineCitation']['Article']:
abstracts.append(
i['MedlineCitation']['Article']['Abstract']['AbstractText'])
else:
abstracts.append('no abstract')
# find type of article
type = i['MedlineCitation']['Article']['PublicationTypeList'][
'PublicationType']
if isinstance(type, dict):
types.append(type['#text'])
# print(ty['#text'])
else:
# print(ty)
type_stripped = []
for d in type:
type_stripped.append(d['#text'])
type_stripped = ', '.join(type_stripped)
types.append(type_stripped)
return titles, abstracts, types, jour_names
def fetch_abstract(self,pmid): handle = efetch(db='pubmed', id=pmid, retmode='xml',email='*****@*****.**',retmax=1000) xml_data = read(handle)[0] try: article = xml_data['MedlineCitation']['Article'] abstract = article['Abstract']['AbstractText'][0] return abstract except (IndexError, KeyError): return None
def fetch_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except IndexError:
return None
def fetch_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = handle.read()
r = re.compile('<AbstractText>(.*?)</AbstractText>')
m = r.search(xml_data)
if m:
abstract = m.group(1)
return abstract
else:
return ""
def fetch_abstract(pmid): # not really being used at all, just a ref. function
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = Entrez.read(handle)
print(xml_data)
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']
return abstract
except IndexError:
return None
def fetch_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)
try:
article = xml_data['PubmedArticle'][0]['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
title = article['ArticleTitle']
return abstract, title
except (IndexError, KeyError) as _:
return None
def fetch_abstract(pmid):
"""Pass in an article id."""
pmid = str(pmid)
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except Exception as e :
return '{}: {}'.format(e.__class__.__name__, e)
def fetch_abstract(pmid):
"""Pass in an article id."""
pmid = str(pmid)
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except Exception as e:
return '{}: {}'.format(e.__class__.__name__, e)
def fetch_abstract(pmid):
Entrez.email = "*****@*****.**"
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except IndexError:
return None
def __init__(self, pmid):
self.pmid = pmid
Entrez.email = '*****@*****.**'
inCache = False
if self.pmid in pmidTermFind.pmid_xmlData_HISTORY: # If ID in cache
self.xml_data = pmidTermFind.pmid_xmlData_HISTORY[self.pmid]
inCache = True
if inCache == False:
self.handle = efetch(db='pubmed', id=self.pmid, retmode='xml')
self.xml_data = Entrez.read(self.handle)[0]
pmidTermFind.pmid_xmlData_HISTORY[pmid] = self.xml_data
def get_mesh_from_pmid(self, user):
Entrez.email = user.email
handle = efetch(db="pubmed", id=str(self.pmid), retmode="xml")
xml_data = read(handle)[0]
# Skips articles without MeSH terms
if u'MeshHeadingList' in xml_data["MedlineCitation"]:
for mesh in xml_data["MedlineCitation"][u'MeshHeadingList']:
major = "N"
qualifiers = mesh[u'QualifierName']
if len(qualifiers) > 0:
major = str(qualifiers[0].attributes.items()[0][1])
descr = mesh[u'DescriptorName']
name = descr.title()
def get_mesh_from_pmid(self, user):
Entrez.email = user.email
handle = efetch(db = "pubmed", id = str(self.pmid), retmode = "xml")
xml_data = read(handle)[0]
# Skips articles without MeSH terms
if u'MeshHeadingList' in xml_data["MedlineCitation"]:
for mesh in xml_data["MedlineCitation"][u'MeshHeadingList']:
major = "N"
qualifiers = mesh[u'QualifierName']
if len(qualifiers) > 0:
major = str(qualifiers[0].attributes.items()[0][1])
descr = mesh[u'DescriptorName']
name = descr.title()
def fetch_article(pmid): """ Test function => Not working """ handle = efetch(db='pubmed', id=pmid, retmode='xml', ) xml_data = read(handle)[0] try: article = xml_data['MedlineCitation']['Article'] abstract = article['Abstract']['AbstractText'][0] return article except IndexError: return None
def add_pmid_article_to_database(article_id):
"""
Given a PMID, use external APIs to get the necessary article data
in order to add the article to our database.
"""
if len(list(get_article_object(article_id))) == 0:
pmid = str(article_id)
handle = efetch("pubmed", id=[pmid], rettype="medline", retmode="text")
records = list(Medline.parse(handle))
records = records[0]
if "TI" not in records:
return False # catch bad PMIDs
article_info = {}
article_info["title"] = records["TI"]
article_info["PMID"] = pmid
article_info["authors"] = ', '.join(records["AU"])
article_info["abstract"] = records["AB"]
article_info["DOI"] = getDOI(records["AID"])
article_info["experiments"] = ""
article_info["metadata"] = str({"meshHeadings": []})
article_info["reference"] = None
identity = ""
try:
article_info["experiments"] = {
"locations":
eval(
urllib.request.urlopen(
"http://neurosynth.org/api/studies/peaks/" +
str(pmid) + "/").read().decode())["data"]
}
k = article_info["experiments"]["locations"]
for i in range(len(k)):
if len(k[i]) == 4:
identity = k[0]
k[i] = k[i][1:]
k[i] = ",".join([str(x) for x in (k[i])])
except BaseException:
pass
article_info["id"] = identity
article_info["experiments"] = [article_info["experiments"]]
Articles.insert(abstract=article_info["abstract"],
authors=article_info["authors"],
doi=article_info["DOI"],
experiments=article_info["experiments"],
pmid=article_info["PMID"],
title=article_info["title"]).execute()
return True
return False
def date(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)['PubmedArticle'][0]
data = xml_data['MedlineCitation']['Article']['Journal']['JournalIssue'][
'PubDate']
if 'Day' in data:
day = data['Day']
else:
day = ''
return data['Year'] + ' ' + data['Month'] + ' ' + day
def get_metadata_from_PMID( pmid ):
"""This function will take an input PMID and parse the attributes I am interested in for the cytoscape plugin...."""
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
verbose_output = False
try:
date_completed = format_ddate( xml_data['MedlineCitation']['DateCompleted'] )
except:
print "Data Completed not available??",pmid
try:
otherID = xml_data['MedlineCitation']['OtherID']
except:
print "Other ID Not availble??",pmid
try:
MeshHeadings = xml_data['MedlineCitation']['MeshHeadingList']
except:
print "Unable to get mesheadings for",pmid
try:
article = xml_data['MedlineCitation']['Article']
if verbose_output: print xml_data
#print date_completed,otherID
for author in article['AuthorList']:
#author_key = { 'LastNAme': author['LastName'], 'Initials': author['Initials'] }
author_key = author['LastName'] + ','+ author['Initials']
#print author['LastName'],author['Initials'],author,'MOO'
if author_key in global_author_list:
global_author_list[ author_key ] +=1
#print "adding author"
else:
global_author_list[ author_key ] = 1
#print "I ADDED AN AUTHOR..."
#return abstract
except IndexError:
return None
except:
print "unable to process",pmid
print "Unexpected error:", sys.exc_info()[0]
try:
abstract = article['Abstract']['AbstractText'][0]
except:
print "Unable to get abstract for",pmid
print "Unexpected error:", sys.exc_info()[0]
def get_mesh(pmid):
# call PubMed API
handle = efetch(db='pubmed', id=str(pmid), retmode='xml')
xml_data = read(handle)[0]
# skip articles without MeSH terms
if u'MeshHeadingList' in xml_data['MedlineCitation']:
for mesh in xml_data['MedlineCitation'][u'MeshHeadingList']:
# grab the qualifier major/minor flag, if any
major = 'N'
qualifiers = mesh[u'QualifierName']
if len(qualifiers) > 0:
major = str(qualifiers[0].attributes.items()[0][1])
# grab descriptor name
descr = mesh[u'DescriptorName']
name = descr.title()
yield(name, major)
def get_article_title(pmid):
"""
Connect to pubmed database and get the article
title of the given pmid.
Return NA if faild
"""
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
xml_data = read(handle)
xml_data = xml_data['PubmedArticle'][0]
try:
title = xml_data['MedlineCitation']['Article']['ArticleTitle']
except:
title = "NA"
return title
def getDocument(self,pmid):
#this method will return all associated attributes for an article
#including Article Title , Publication Date , Authors' Names , Citations......etc.
# it will return it as a python dictionary suitable for storage in mongodb
handle = efetch(db='pubmed', id=str(pmid), retmode='xml')
xml_data = read(handle)[0]
article = dict(id = pmid,Title = str(xml_data['MedlineCitation']['Article'][u'ArticleTitle'])
, Abstract=str(self.safeAbstract(xml_data['MedlineCitation'],u'Abstract')),
DateCompleted="{}/{}/{}".format(self.safeDateCompleted(xml_data['MedlineCitation'],'DateCompleted','Day'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateCompleted','Month'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateCompleted','Year'),),
DateRevised="{}/{}/{}".format(self.safeDateCompleted(xml_data['MedlineCitation'],'DateRevised','Day'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateRevised','Month'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateRevised','Year')))
return (xml_data,article)
def fetch_article(pmid): """ Test function => Not working """ handle = efetch(db='pubmed', id=pmid, retmode='xml', ) informations = read(handle) stuff = informations[u'PubmedArticle'][0] date = stuff[u'PubmedData']["History"][1] month = date[u'Month'] day = date[u'Day'] year = date[u'Year'] print month print day print year return "choucroute"
def fetch_abstract(pmid): """ Retrun abstract of a given article using pmid => Return None when pmid can't be return (can happen when article is in chinese) """ handle = efetch(db='pubmed', id=pmid, retmode='xml', ) xml_data = read(handle) xml_data = xml_data['PubmedArticle'][0] try: article = xml_data['MedlineCitation']['Article'] abstract = article['Abstract']['AbstractText'][0] return abstract except IndexError: return None except KeyError: return None
def get_country_publication_stat(run_folder):
##
## Get the publications stats for country.
## get the list of pmid retrieved from the
## meta folder and connect to the NCBI to fecth
## publications informations, parse it to get the
## country of publication.
##
## return a dictionnary
##
## init structure
country_to_count = {}
## get list of PMID to process
meta_file_list = glob.glob(run_folder+"/meta/*.csv")
for meta_file in meta_file_list:
meta_file_in_array = meta_file.split("/")
file_name = meta_file_in_array[-1]
file_name_in_array = file_name.split(".")
pmid = file_name_in_array[0]
## get country publication
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml', )
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
country = stuff[u'MedlineCitation'][u'MedlineJournalInfo'][u'Country']
print country # to delete
except:
country = "NA"
## fill dictionnary
if(country not in country_to_count.keys()):
country_to_count[country] = 1
else:
country_to_count[country] += 1
return country_to_count
def fetch_abstract(pmid):
'''
This method was originally written by Karol. Colin Hortman added the try/except block to handle articles that
abstracts were not found
http://stackoverflow.com/questions/17409107/obtaining-data-from-pubmed-using-python
:param pmid: PubMed ID
:return: Abstract ( will return as StringElement but behaves mostly like a string )
'''
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = Entrez.read(handle)[0]
try:
article = xml_data['MedlineCitation']['Article']
try:
abstract = article['Abstract']['AbstractText'][0]
return abstract
except KeyError:
pass
#print('No Abstract found for PMID: ', pmid)
except IndexError:
return None
return None
def date(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)['PubmedArticle']
if xml_data:
xml_list = xml_data[0]
data = xml_list['MedlineCitation']['Article']['Journal'][
'JournalIssue']['PubDate']
if 'Day' in data:
day = data['Day']
else:
day = ''
if 'Month' in data:
month = data['Month'] + ' '
else:
month = ''
if 'Year' in data:
year = data['Year'] + ' '
else:
year = ''
return year + month + day
else:
return ''
def fetch_article(pmid):
"""
Test function
=> Not working
"""
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
print month
print day
print year
return "choucroute"
def parse_pmc_ids(self, pmcid, retmode='xml'):
#get xml from pmc
handle = efetch(db='pmc', id=pmcid, retmode=retmode)
xml_string = handle.read()
xml = ET.fromstring(xml_string)
#check for keywords and MeshTerms
keys = []
for art in xml.getchildren():
#title
title = ''.join(art.xpath('.//article-meta//article-title/text()'))
#authors
auth = zip(
art.findall('.//*[@contrib-type="author"]/name/given-names'),
art.findall('.//*[@contrib-type="author"]/name/surname'))
auth = ';'.join([' '.join([i.text, j.text]) for (i, j) in auth])
#affiliations
aff = ';'.join(art.xpath('.//aff/text()'))
#publication_date
pub_date = '-'.join(
art.xpath('.//article-meta/pub-date[@pub-type="epub"]/*/text()'
)[::-1])
#ids
##pubmed_id
pubmed_id = ''.join(
art.xpath(
'.//article-meta/article-id[@pub-id-type="pmid"]/text()'))
##doi
doi = ''.join(
art.xpath(
'.//article-meta/article-id[@pub-id-type="doi"]/text()'))
##pmcid
pmcid = ''.join(
art.xpath(
'.//article-meta/article-id[@pub-id-type="pmc"]/text()'))
#abstract
abstract = ''.join(art.xpath('.//abstract//*/text()'))
#fulltext
full_text = ''.join(art.xpath('.//body//*/text()'))
#journal
journal = ''.join(
art.xpath(
'//journal-meta/journal-id[@journal-id-type="iso-abbrev"]/text()'
))
#pmcclass
pmcclass = ''.join(
art.xpath(
'.//article-meta/article-categories//subject/text()'))
#pmc_keywords
pmc_keywords = art.xpath('.//kwd/text()')
keys.append((pmcid, title, auth, aff, pub_date, pubmed_id, doi,
abstract, full_text, journal, pmcclass, pmc_keywords))
return keys, xml_string
def add_pmid_article_to_database(article_id):
"""
Given a PMID, use external APIs to get the necessary article data
in order to add the article to our database.
"""
pmid = str(article_id)
try:
handle = efetch("pubmed", id=[pmid], rettype="medline", retmode="text")
except BaseException:
return False # Could not access correct pubmed ID
records = list(Medline.parse(handle))
records = records[0]
article_info = {}
article_info["title"] = records.get("TI")
article_info["PMID"] = pmid
article_info["authors"] = ', '.join(records.get("AU", []))
article_info["abstract"] = records.get("AB")
article_info["DOI"] = getDOI(records.get("AID", []))
article_info["experiments"] = []
article_info["metadata"] = str({"meshHeadings": []})
article_info["reference"] = None
identity = ""
try:
locations_list = eval(
urllib.request.urlopen(
"http://neurosynth.org/api/studies/peaks/" +
str(pmid) +
"/").read().decode())["data"]
id_map = {}
greatest_id = 89999
current_exp = None
for loc in locations_list:
current_loc_id = None
vals = loc
if len(loc) == 4:
current_loc_id = loc[0]
vals = vals[1:]
# vals is the x, y, z array; current_loc_id is the Neurosynth ID
if current_loc_id not in id_map:
greatest_id += 1
id_map[current_loc_id] = greatest_id
if current_exp is not None:
# Add the current experiment if its not None
article_info["experiments"].append(current_exp)
current_exp = {
"caption": "",
"locations": [],
"descriptors": [],
"contrast": "",
"space": "",
"effect": ""
}
current_exp["locations"].append(",".join([str(v) for v in vals]))
if current_exp is not None:
article_info["experiments"].append(current_exp)
except BaseException:
pass
Articles.create(abstract=article_info["abstract"],
authors=article_info["authors"],
doi=article_info["DOI"],
experiments=str(article_info["experiments"]),
pmid=article_info["PMID"],
title=article_info["title"])
return True
def fetch_abstract():
colnames = ['pmids']
data = pandas.read_csv("pmids.csv", names=colnames)
pmids = list(data.pmids)
full = []
for i in pmids:
handle = efetch(db='pubmed', id=i, retmode='xml')
xml_data = handle.read()
soup = BeautifulSoup(xml_data)
a_recs = []
for tag in soup.findAll("pubmedarticle"):
title = tag.articletitle.text
journal = tag.findAll("journal")
try:
for info in journal:
year = info.find("year").text
except:
for info in journal:
year = info.find("year")
for a_tag in tag.findAll("author"):
a_rec = {}
a_rec['year'] = year
a_rec['title'] = title
a_rec['pmid'] = int(tag.pmid.text)
try:
a_rec['lastname'] = str(a_tag.lastname.text)
except:
a_rec['lastname'] = str(a_tag.lastname)
try:
a_rec['forename'] = str(a_tag.forename.text)
except:
a_rec['forename'] = str(a_tag.forename)
try:
a_rec['initials'] = str(a_tag.initials.text)
except:
a_rec['initials'] = str(a_tag.initials)
try:
a_rec['affiliation'] = str(a_tag.affiliation.text)
except:
a_rec['affiliation'] = str(a_tag.affiliation)
a_recs.append(a_rec)
#full.append(a_recs)
full.append(a_recs)
def convert(input):
if isinstance(input, dict):
return {convert(key): convert(value) for key, value in input.iteritems()}
elif isinstance(input, list):
return [convert(element) for element in input]
elif isinstance(input, unicode):
return input.encode('utf-8')
else:
return input
full = convert(full)
print "running"
#article = ET.XML(xml_data)
#print article.find('AuthorList').findall('Author')
for entry in full:
#print entry
keys = ['year', 'title', 'pmid', 'lastname', 'forename', 'initials', 'affiliation']
f = open("citations.csv", "ab")
dict_writer = csv.DictWriter(f, keys)
dict_writer.writer.writerow(keys)
dict_writer.writerows(entry)
return
def fetch_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='text', rettype='abstract')
data = handle.read()
return data
hit_gi = int(aln.hit_id.split("|")[1])
if hit_gi in processed_ids:
continue
else:
processed_ids[hit_gi] = None
if "LOW QUALITY" in aln.hit_def:
print '\t\t"LOW QUALITY" in seq name'
continue
for hsp in aln.hsps:
if float(hsp.expect) < EXPECT:
qlen = len(hsp.query.replace("-",""))
coverage = (float(qlen)/ float(record.query_letters))*100.0
if coverage > 80.0:
sleep(5)
print "\t\tExp: %f start: %i Qend: %i Coverage%%: %f" % (float(hsp.expect), hsp.query_start, hsp.query_end, coverage)
ef_handle = efetch(db="protein", id=hit_gi, rettype="fasta", retmode="text")
fasta_txt = ef_handle.read()
while fasta_txt.find("unavailable") >=0:
print "\t\tentrez is failing hard. sleeping. (id:%i)" % hit_gi
sleep(5)
ef_handle = efetch(db="protein", id=hit_gi, rettype="fasta", retmode="text")
fasta_txt = ef_handle.read()
# edit fasta_txt
fasta_lines = fasta_txt.splitlines()
fasta_header = fasta_lines[0]
fasta_seq = "".join(fasta_lines[1:])
fasta_header = fasta_header.replace('predicted protein', 'pred.')
fasta_header = fasta_header.replace('hypothetical protein', 'pred.')
fasta_header = fasta_header.replace('PREDICTED:', 'pred.')
def collect_NCBI():
global all_pmids
global pmid_dict
if os.path.exists(f'./{rel_name}/{rel_name}_pmid_dict.json'):
with open(f'./{rel_name}/{rel_name}_pmid_dict.json', 'r') as f:
jd = f.read()
temp_dict = json.loads(jd)
pmid_dict.update(temp_dict)
return pmid_dict
for idx in tqdm(range(len(all_pmids))):
pmid = all_pmids[idx]
# get records for each pmid
fetch_records_handle1 = efetch(db="pubmed",
id=str(pmid),
rettype="medline",
retmode="text")
# parse fetched records
records1 = Medline.parse(fetch_records_handle1)
# Need to iterate over records to extract information
for record1 in records1:
# try except check to be sure that NCBI is not returning empty result
try:
# let's get pmcid if exists
id2 = record1['PMC'][3:]
#print('PMC',id2)
# get records for pmcid
fetch_records_handle2 = efetch(db="pubmed",
id=str(id2),
rettype="medline",
retmode="text")
# parse records for pmcid
records2 = Medline.parse(fetch_records_handle2)
# Need to iterate over records to extract information
'''
Collect following information: authors, authors' affiliations, publication date, citations, grants
Store all these information in an dictionary (pmid_dict)
'''
for record2 in records2:
authors = record2['FAU']
affiliations = record2['AD']
pub_date = record2['DCOM']
citations = get_links_id(pmid)
grants = record2['GR']
pmid_dict[pmid] = {
'pmcid_number': id2,
'pmcid': True,
'authors': authors,
'affiliations': affiliations,
'grants': grants,
'pub_date': pub_date,
'citations': citations
}
except:
authors = record1['FAU']
try:
affiliations = record1['AD']
except:
affiliations = ''
try:
pub_date = record1['DCOM']
except:
pub_date = ''
try:
citations = get_links_id(pmid)
except:
citations = ''
try:
grants = record1['GR']
except:
grants = ''
pmid_dict[pmid] = {
'pmcid_number': '',
'pmcid': False,
'authors': authors,
'affiliations': affiliations,
'grants': grants,
'pub_date': pub_date,
'citations': citations
}
with open(f'./{rel_name}/{rel_name}_pmid_dict.json', 'w') as output:
output.write(json.dumps(pmid_dict))
return pmid_dict
csvfile.writerow(['Quad', 'Quad_Alpha', 'V1', 'V2', 'V3', 'V4', 'L1', 'L2', 'L3', 'L4', 'L5', 'L6'])
for x,y in zip(verts, edge_lengths):
quad = [TOLC[atoms[z]['res']] for z in x]
resverts = [atoms[z]['resseq'] for z in x]
csvfile.writerow([''.join(quad), ''.join(sorted(quad))]+resverts+y)
# removing very irregular tetrahedra
if task_profile or task_threading:
# Calculate potentials
orig_sim_pot = simplex_potential([[atoms[x]['res'] for x in y] for y in verts])
orig_res_pot = residue_potential(len(atoms), verts, orig_sim_pot)
if task_threading:
if seq_download:
seqfile = efetch(db="protein", id=seq_id, rettype=seq_format)
else:
seqfile = seq_filename
ids,seq = zip(*[(x.id, x.seq) for x in AlignIO.read(seqfile,seq_format)])
# Make PDB sequence the first one
idtemp,seqtemp = [(x,y) for x,y in zip(ids, seq) if PDB_id in x][0]
ids,seq = zip(*[(x,y) for x,y in zip(ids, seq) if PDB_id not in x])
ids = [idtemp] + list(ids)
seq = [seqtemp] + list(seq)
trimmed_nums, trimmed_seq = zip(*[trim_gaps(x,seq[0], number_sequence(x)) for x in seq])
dqs = []
for z,i,d in zip(trimmed_seq[1:], trimmed_nums[1:], ids[1:]):
res_numbers,residues = thread_sequence(z, trimmed_seq[0], pdbdata['DBREF']['seqbegin'], i, span)
mut_sim_pot = simplex_potential([[residues[x] for x in y] for y in verts])
def print_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='text', rettype='abstract')
print handle.read()
def get_metadata_from_PMID( pmid, output_errors=False, dump_xml=False ):
"""This function will take an input PMID and parse the attributes I am interested in for the cytoscape plugin...."""
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
verbose_output = False
# output_errors= False
author_affiliation_list = []
cur_paper_author_list = []
try:
date_completed = format_ddate( xml_data['MedlineCitation']['DateCompleted'] )
except:
print "Date Completed not available??",pmid
## Will try date created
#date_completed = None
#date_created = format_ddate( xml_data['MedlineCitation']['DateCreated'] )
## I am removing the difference between date completed and created-- it doens't really matter for my purposes
date_completed = format_ddate( xml_data['MedlineCitation']['DateCreated'] )
#fp_error.write('Date Completed Not Avaiable:\n'+str(xml_data)+'\n\n')
try:
otherID = xml_data['MedlineCitation']['OtherID']
except:
print "Other ID Not availble??",pmid
try:
MeshHeadings = xml_data['MedlineCitation']['MeshHeadingList']
except:
print "Unable to get mesheadings for",pmid
if output_errors: fp_error.write('MESH NOT AVAIABLE:\n'+str(xml_data)+'\n\n')
try:
article = xml_data['MedlineCitation']['Article']
if verbose_output: print xml_data
for author in article['AuthorList']:
#author_key = { 'LastNAme': author['LastName'], 'Initials': author['Initials'] }
#print author
if 'LastName' in author:
author_key = author['LastName'] + ','+ author['Initials']
#print author,author_key
cur_paper_author_list.append(author_key)
elif 'CollectiveName' in author:
print "FOUND A COLLECTION EXAMPLE",author
if 'Affiliation' in author:
author_affil = author['Affiliation']
author_affiliation_list.append( (author, author_affil) )
# print author_affil
# sys.exit()
except NameError as e:
print e
except IndexError:
return None
except:
print "unable to proces article tag",pmid
print "Unexpected error parsing author string:", sys.exc_info()[0]
if output_errors: fp_error.write('Article NOT AVAILABLE\n'+str(xml_data)+'\n\n')
print author
#print xml_data
try:
abstract = article['Abstract']['AbstractText'][0]
except:
print "Unable to get abstract for",pmid
if dump_xml:
print xml_data
return xml_data
else:
return { 'auth_list': cur_paper_author_list, 'affiliations': author_affiliation_list, 'publication_date': date_completed }
def evaluate_article(pmid):
##
## [IN PROGRESS]
##
## -> Test if the abstract is cool
## -> return true or false
##
## TODO : write doc
##
##------------------------##
## Parameters for filters ##
##------------------------##
## initialize parameters
oldest_year_authorized = "NA"
case_report_only = False
case_report_check = False
authorized_languages = []
valid_article = False
check_date = True
check_language = True
validation_check = {}
validation_keywords = {}
exclusion_check = {}
exclusion_keywords = {}
exclusion_keywords_found = False
## test if config file exist
if (os.path.isfile("config.conf")):
config_data = open("config.conf", "r")
validation_keywords_cmpt = 0
exclusion_keywords_cmpt = 0
for line in config_data:
line = line.replace("\n", "")
line_in_array = line.split(";")
if (line_in_array[0] == "min year"):
oldest_year_authorized = line_in_array[1]
elif (line_in_array[0] == "authorized languages"):
languages_list = line_in_array[1].split(",")
for elt in languages_list:
authorized_languages.append(unicode(elt))
elif (line_in_array[0] == "validation keywords"):
validation_keywords_cmpt += 1
validation_check["keywords_" +
str(validation_keywords_cmpt)] = False
validation_keywords["keywords_" +
str(validation_keywords_cmpt)] = []
keywords_list = line_in_array[1].split(",")
for elt in keywords_list:
if (elt not in validation_keywords[
"keywords_" + str(validation_keywords_cmpt)]):
validation_keywords[
"keywords_" +
str(validation_keywords_cmpt)].append(str(elt))
## Retrieve Exclusion list
elif (line_in_array[0] == "exclusion keywords"):
exclusion_keywords_found = True
exclusion_keywords_cmpt += 1
exclusion_check["exclusion_" +
str(exclusion_keywords_cmpt)] = False
exclusion_keywords["exclusion_" +
str(exclusion_keywords_cmpt)] = []
keywords_list = line_in_array[1].split(",")
for elt in keywords_list:
if (elt not in exclusion_keywords[
"exclusion_" + str(exclusion_keywords_cmpt)]):
exclusion_keywords[
"exclusion_" +
str(exclusion_keywords_cmpt)].append(str(elt))
## case report only option
## if nothing is set, default is False
elif (line_in_array[0] == "case report only"
and str(line_in_array[1]) == "True"):
case_report_only = True
config_data.close()
## default configuration
else:
oldest_year_authorized = 2008
authorized_languages = [u'eng']
validation_check["keywords_1"] = False
validation_check["keywords_2"] = False
validation_keywords["keywords_1"] = [
"algorithm", "machine"
"learning", "neural", "network", "statistic", "deep",
"classification", "model"
]
validation_keywords["keywords_2"] = [
"Sjogren", "sjogren", "lupus", "autoimmunity", "rhumatoid",
"arthrisis", "RA", "SjS", "SLE"
]
exclusion_check["exclusion_1"] = False
exclusion_keywords["exclusion_1"] = []
if (not exclusion_keywords_found):
exclusion_check["exclusion_1"] = False
exclusion_keywords["exclusion_1"] = []
##---------------##
## The Easy Part ##
##---------------##
## get meta data on the articles
try:
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
## get date from the history attribute, select
## the date of acceptation.
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
## get the name of the review
journal_name = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'MedlineJournalInfo'][u'MedlineTA']
## get the keywords for the articles
## the format is a bit strange, may have to be carreful
## with this data (mix of strings and unicode elements)
keywords_list = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'KeywordList']
## Get the author's conflict of interest,
## because we can.
try:
conflict_of_interest = informations[u'PubmedArticle'][0][
u'MedlineCitation'][u'CoiStatement']
except:
conflict_of_interest = "NA"
## Get title of the article
article_title = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'Article'][u'ArticleTitle']
## Get language of the article
article_language = informations[u'PubmedArticle'][0][
u'MedlineCitation'][u'Article'][u'Language'][0]
## Get country of publications
country = stuff[u'MedlineCitation'][u'MedlineJournalInfo'][u'Country']
except:
return (False, False, False)
##----------------##
## The Smart Part ##
##----------------##
## run further analysis on the abstract using nltk
##
## WORKING ON EXCLUSION LIST
##
## fetch the abstract and convert it to
## a nltk text object.
abstract_file_name = "abstract/" + str(pmid) + "_abstract.txt"
abstract = fetch_abstract(pmid)
if (abstract):
save_abstract(abstract, abstract_file_name)
abstract_text = load_text(abstract_file_name)
## Play with tokenization and chunking
## Get all the commun names in the abstract
names_found_in_abstract = []
try:
tokens = nltk.word_tokenize(abstract.encode('utf8'))
tagged = nltk.pos_tag(tokens)
entities = nltk.chunk.ne_chunk(tagged)
except:
print "[WARNINGS] => can't perform nlp operation"
entities = []
for item in entities:
try:
if (item[1] in ["NN", "NNS", "NNP"]):
if (item[0] not in names_found_in_abstract):
names_found_in_abstract.append(item[0])
except:
## Somethig went wrong
choucroute = True
## Check validation list
for item in names_found_in_abstract:
for key in validation_keywords.keys():
keywords_validation_list = validation_keywords[key]
if (item in keywords_validation_list):
validation_check[key] = True
## Check exclusion list
for item in names_found_in_abstract:
for key in exclusion_keywords.keys():
exclusion_validation_list = exclusion_keywords[key]
if (item in exclusion_validation_list):
exclusion_check[key] = True
## Check if is a case report
if (case_report_only):
print "[DEBUG] => Case report only"
if (article_is_a_case_report(abstract_file_name)):
case_report_check = True
##--------------##
## PASS OR FAIL ##
##--------------##
## General check phase
easy_check_passed = False
smart_check_passed = True
## Basic check on meta data
## - check date
if (int(year) < int(oldest_year_authorized)):
check_date = False
## - check language
if (article_language not in authorized_languages):
check_language = False
## Easy Filter
if (check_date and check_language):
easy_check_passed = True
## Complex filter (inclusion)
if (False in validation_check.values()):
smart_check_passed = False
## Complex filter (exclusion)
if (True in exclusion_check.values()):
smart_check_passed = False
## Case reprot filter
if (case_report_only and case_report_check):
print "[DEBUG] => EXLUDED"
smart_check_passed = False
## Global check
if (easy_check_passed and smart_check_passed):
valid_article = True
##-------------##
## SAVING DATA ##
##-------------##
## Write and delete files
if (valid_article):
## Save meta data in a text file
## for further use
title_line = u'>Title;' + unicode(article_title) + u"\n"
date_line = u'>Date;' + unicode(day) + u"/" + unicode(
month) + u"/" + unicode(year) + u"\n"
#date_line = '>Date;'+str(day.encode('utf8'))+"/"+str(month.encode(utf8))+"/"+str(year.encode("utf8"))+"\n"
journal_line = u">Journal;" + unicode(journal_name) + u"\n"
country_line = u">Country;" + unicode(country) + u"\n"
conflict_of_interest_line = u">Conflict;" + unicode(
conflict_of_interest) + u"\n"
meta_data = open("meta/" + str(pmid) + ".csv", "w")
meta_data.write(title_line.encode('utf8'))
meta_data.write(date_line.encode('utf8'))
meta_data.write(journal_line.encode('utf8'))
meta_data.write(country_line.encode('utf8'))
meta_data.write(conflict_of_interest_line.encode('utf8'))
meta_data.close()
else:
## Delete the abstract
try:
if (abstract):
os.remove(abstract_file_name)
except:
print "[WARNING] => can't delete " + str(abstract_file_name)
##------------------##
## RETURN SOMETHING ##
##------------------##
## return True if the article pass the
## evaluation, else False.
return (valid_article, easy_check_passed, smart_check_passed)
def parse_pubmed_ids(cls, pub_id, retmode='xml'):
#get xml from pubmed
handle = efetch(db='pubmed', id=pub_id, retmode=retmode)
xml_string = handle.read()
xml = ET.fromstring(xml_string)
#check for keywords and MeshTerms
keys = []
for art in xml.getchildren():
#authors
auth = zip(art.findall('.//Author/ForeName'),
art.findall('.//Author/LastName'))
auth = ';'.join([' '.join([i.text, j.text]) for (i, j) in auth])
#affiliations
email_re = re.compile('Electronic address.*\.')
aff = ';'.join(
np.unique([
email_re.sub('', aff.text).strip(string.punctuation + ' ')
for aff in art.findall('.//Affiliation')
]))
#publication date
pub_date = '-'.join([
x.text for x in [
art.find('.//PubMedPubDate[@PubStatus="entrez"]/Year'),
art.find('.//PubMedPubDate[@PubStatus="entrez"]/Month'),
art.find('.//PubMedPubDate[@PubStatus="entrez"]/Day')
]
])
if pub_date == '':
pub_date = '1900-01-01'
pubmed_id = ''.join(
art.xpath(
'.//PubmedData/ArticleIdList/ArticleId[@IdType="pubmed"]/text()'
))
doi = ''.join(
art.xpath(
'.//PubmedData/ArticleIdList/ArticleId[@IdType="doi"]/text()'
))
pmcid = ''.join(
art.xpath(
'.//PubmedData/ArticleIdList/ArticleId[@IdType="pmc"]/text()'
))
title = ''.join(art.xpath('.//ArticleTitle/text()')).strip()
abstract = ''.join(art.xpath('.//AbstractText/text()'))
journal = ''.join(art.xpath('.//Journal/ISOAbbreviation/text()'))
pubmed_class = ','.join(art.xpath('.//PublicationType/text()'))
keys.append((
pubmed_id, title, auth, aff, pub_date, abstract, doi, pmcid,
journal, pubmed_class,
art.xpath('MedlineCitation/KeywordList/*/text()'),
art.xpath(
'MedlineCitation/MeshHeadingList/MeshHeading/*[@MajorTopicYN="Y"]/../DescriptorName/text()'
),
art.xpath(
'MedlineCitation/MeshHeadingList/MeshHeading/*[@MajorTopicYN="Y"]/../QualifierName/text()'
)))
# df = pd.DataFrame(keys, columns=['pubmed_id', 'pub_date', 'keywords', 'mesh_descriptors', 'mesh_qualifiers'])
# df.pub_date = pd.to_datetime(df.pub_date)
gc.collect()
return keys, xml_string
def evaluate_article(pmid):
##
## [IN PROGRESS]
##
## -> Test if the abstract is cool
## -> return true or false
##
##------------------------##
## Parameters for filters ##
##------------------------##
oldest_year_authorized = 2008
authorized_languages = [u'eng']
valid_article = False
check_date = True
check_language = True
validation_check_keywords_1 = False
validation_check_keywords_2 = False
##---------------##
## The Easy Part ##
##---------------##
## get meta data on the articles
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml', )
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
## get date from the history attribute, select
## the date of acceptation.
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
## get the name of the review
journal_name = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'MedlineJournalInfo'][u'MedlineTA']
## get the keywords for the articles
## the format is a bit strange, may have to be carreful
## with this data (mix of strings and unicode elements)
keywords_list = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'KeywordList']
## Get the author's conflict of interest,
## because we can.
try:
conflict_of_interest = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'CoiStatement']
except:
conflict_of_interest = "NA"
## Get title of the article
article_title = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'Article'][u'ArticleTitle']
## Get language of the article
article_language = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'Article'][u'Language'][0]
except:
return (False,False,False)
##----------------##
## The Smart Part ##
##----------------##
## run further analysis on the abstract using nltk
## fetch the abstract and convert it to
## a nltk text object.
abstract_file_name = "abstract/"+str(pmid)+"_abstract.txt"
abstract = fetch_abstract(pmid)
if(abstract):
save_abstract(abstract, abstract_file_name)
abstract_text = load_text(abstract_file_name)
## Play with tokenization and chunking
## Get all the commun names in the abstract
names_found_in_abstract = []
try:
tokens = nltk.word_tokenize(abstract.encode('utf8'))
tagged = nltk.pos_tag(tokens)
entities = nltk.chunk.ne_chunk(tagged)
except:
print "[WARNINGS] => can't perform nlp operation"
entities = []
for item in entities:
try:
if(item[1] in ["NN", "NNS", "NNP"]):
if(item[0] not in names_found_in_abstract):
names_found_in_abstract.append(item[0])
except:
## Somethig went wrong
choucroute = True
## -> Biology keywords check
## -> Artificial intelligence keywords check
IA_keywords = ["algorithm", "machine" "learning", "neural", "network", "statistic", "deep", "classification", "model"]
Clinical_keywords = ["Sjogren" ,"sjogren", "lupus", "autoimmunity", "rhumatoid", "arthrisis", "RA", "SjS", "SLE"]
for item in names_found_in_abstract:
if(item in IA_keywords):
validation_check_keywords_1 = True
if(item in Clinical_keywords):
validation_check_keywords_2 = True
##--------------##
## PASS OR FAIL ##
##--------------##
## General check phase
easy_check_passed = False
smart_check_passed = False
## Basic check on meta data
## - check date
if(int(year) < int(oldest_year_authorized)):
check_date = False
## - check language
if(article_language not in authorized_languages):
check_language = False
## Easy Filter
if(check_date and check_language):
easy_check_passed = True
## Complex filter
if(validation_check_keywords_1 and validation_check_keywords_2):
smart_check_passed = True
## Global check
if(easy_check_passed and smart_check_passed):
valid_article = True
##-------------##
## SAVING DATA ##
##-------------##
## Write and delete files
if(valid_article):
## Save meta data in a text file
## for further use
title_line = u'>Title;'+unicode(article_title)+u"\n"
date_line = u'>Date;'+unicode(day)+u"/"+unicode(month)+u"/"+unicode(year)+u"\n"
journal_line = u">Journal;"+unicode(journal_name)+u"\n"
conflict_of_interest_line = u">Conflict;"+unicode(conflict_of_interest)+u"\n"
meta_data = open("meta/"+str(pmid)+".csv", "w")
meta_data.write(title_line.encode('utf8'))
meta_data.write(date_line.encode('utf8'))
meta_data.write(journal_line.encode('utf8'))
meta_data.write(conflict_of_interest_line.encode('utf8'))
meta_data.close()
else:
## Delete the abstract
try:
if(abstract):
os.remove(abstract_file_name)
except:
print "[WARNING] => can't delete "+str(abstract_file_name)
##------------------##
## RETURN SOMETHING ##
##------------------##
## return True if the article pass the
## evaluation, else False.
return (valid_article, easy_check_passed, smart_check_passed)
