def build_exact_sets(o,u):
sets = []
mids = o.get_ids()
print(len(mids))
n = 0
now = dt.now()
for mid in mids:
if n % 100 == 0 and n > 0:
later = dt.now()
delt = (later-now).seconds
f = n / len(mids)
print(f'{n}/{len(mids)} = {f} in {delt} s')
print(f' estimated time remaining = {delt * (1-f)/(f)}')
#FWIW, ICD codes tend to be mapped to multiple MONDO identifiers, leading to mass confusion. So we
#just excise them here. It's possible that we'll want to revisit this decision in the future. If so,
#then we probably will want to set a 'glommable' and 'not glommable' set.
print(mid)
dbx = [ Text.upper_curie(x) for x in o.get_exact_matches(mid) ]
print(dbx)
dbx = set( filter( lambda x: not x.startswith('ICD'), dbx ) )
label = u.get_label(mid)
print(label)
mid = Text.upper_curie(mid)
dbx.add(LabeledID(mid,label))
sets.append(dbx)
n += 1
return sets
def build_sets(o, ignore_list = ['ICD']):
sets = []
mids = o.get_ids()
for mid in mids:
#FWIW, ICD codes tend to be mapped to multiple MONDO identifiers, leading to mass confusion. So we
#just excise them here. It's possible that we'll want to revisit this decision in the future. If so,
#then we probably will want to set a 'glommable' and 'not glommable' set.
dbx = set([Text.upper_curie(x) for x in o.get_xrefs(mid) if not reduce(lambda accumlator, ignore_prefix: accumlator or x.startswith(ignore_prefix) , ignore_list, False)])
dbx = set([norm(x) for x in dbx])
label = o.get_label(mid)
mid = Text.upper_curie(mid)
dbx.add(LabeledID(mid,label))
sets.append(dbx)
return sets
def gene_to_drug_expanded(self, gene_node):
output = []
identifiers = gene_node.get_synonyms_by_prefix('NCBIGENE')
for identifier in identifiers:
unique = set()
geneid = Text.un_curie(identifier)
url = f"{self.url}CTD_chem_gene_expanded_geneID/ncbigene:{geneid}/"
obj = requests.get (url).json ()
for r in obj:
good_row, predicate_label, props, pmids = self.check_expanded_gene_chemical_row(r)
if not good_row:
continue
predicate = self.normalize_predicate(
LabeledID(identifier=f"CTD:{Text.snakify(predicate_label)}", label=predicate_label)
)
#Should this be substance?
drug_node = KNode(Text.upper_curie(r['chemicalID']), type=node_types.CHEMICAL_SUBSTANCE, name=r['chem_label'])
direction = r['direction']
if direction == '->':
subject = drug_node
object = gene_node
else:
subject = gene_node
object = drug_node
edge = self.create_edge(subject,object,'ctd.gene_to_drug_expanded',identifier,predicate,properties = props,url=url,publications=pmids)
#This is what we'd like it to be, but right now there's not enough real specificity on the predicates
#key = (drug_node.id, edge.standard_predicate.label)
key = (drug_node.id, edge.original_predicate.label)
if key not in unique:
output.append( (edge,drug_node) )
unique.add(key)
return output
def drug_to_gene_expanded(self, drug):
output = []
identifiers = drug.get_synonyms_by_prefix('MESH')
for identifier in identifiers:
url=f"{self.url}CTD_chem_gene_expanded_chemicalID/mesh:{Text.un_curie(identifier)}/"
result = requests.get(url)
obj=result.json()
for r in obj:
good_row, predicate_label, props, pmids = self.check_expanded_gene_chemical_row(r)
if not good_row:
continue
predicate = self.normalize_predicate(
LabeledID(identifier=f"CTD:{Text.snakify(predicate_label)}", label=predicate_label)
)
gene_node = KNode(Text.upper_curie(r['geneID']), name=r['gene_label'],type=node_types.GENE)
direction = r['direction']
if direction == '->':
subject = drug
object = gene_node
else:
subject = gene_node
object = drug
edge = self.create_edge(subject,object,'ctd.drug_to_gene_expanded',identifier,predicate,publications=pmids,properties=props,url=url )
output.append( (edge,gene_node) )
return output
def check_expanded_gene_chemical_row(self, r):
props = {"description": r['interaction'], 'taxon': f"taxon:{r['taxonID']}"}
pmids = r['PMID'].split('|')
predicate_label = r['interaction']
# there are lots of garbage microarrays with only one paper. THey goop the place up
# ignore them
good_row = True
if len(pmids) < 3:
if predicate_label in ['affects expression of', 'increases expression of',
'decreases expression of', 'affects methylation of',
'increases methylation of', 'decreases methylation of',
'affects molecular modification of',
'increases molecular modification of',
'decreases molecular modification of']:
good_row = False
if len(pmids) < 2:
if predicate_label in ['affects splicing of', 'increases splicing of', 'decreases splicing of']:
good_row = False
pmids = [Text.upper_curie(p) for p in pmids]
return good_row, predicate_label, props, pmids
