def test():
"""What do we get back for HBB"""
b = Biolink(ServiceContext.create_context())
relations = b.gene_get_disease(('HGNC:4827', ))
checker = Mondo(ServiceContext.create_context())
for p, a in relations:
igc, nid = checker.is_genetic_disease(a)
print(a['id'], igc, nid)
def test_one(infname, outfname, fieldnum):
m = Mondo(ServiceContext.create_context())
n_good = 0
n_bad = 0
diseases = set()
with open(infname, 'r') as inf, open(outfname, 'w') as outf:
h = inf.readline()
for line in inf:
if line.startswith('#'):
continue
x = line.strip().split('\t')[fieldnum]
if x in diseases:
continue
diseases.add(x)
result = m.search(x)
if len(result) == 0:
mondos = ''
names = ''
doids = ''
umlss = ''
efos = ''
n_bad += 1
else:
n_good += 1
mondos = ';'.join(result)
names = ';'.join([m.get_label(r) for r in result])
doids = ';'.join(sum([m.mondo_get_doid(r) for r in result],
[]))
umlss = ';'.join(sum([m.mondo_get_umls(r) for r in result],
[]))
efos = ';'.join(sum([m.mondo_get_efo(r) for r in result], []))
outf.write('{}\t{}\t{}\t{}\t{}\n'.format(x, mondos, doids, umlss,
efos))
print('Good: {} Bad: {}'.format(n_good, n_bad))
def test2():
from greent.service import ServiceContext
cb = ChemBioKS(ServiceContext.create_context())
with open('q2-drugandcondition-list.txt', 'r') as inf:
h = inf.readline()
uniq = set()
for line in inf:
x = line.split('\t')[0]
uniq.add(x)
n_no_pub = 0
n_no_ncbi = 0
for name in uniq:
input_node = KNode("DRUG_NAME:{}".format(name), node_types.DRUG_NAME)
try:
drug_node = cb.graph_drugname_to_pubchem(input_node)[0][1]
ident = drug_node.identifier
ncbi_nodes = cb.graph_pubchem_to_ncbigene(drug_node)
if len(ncbi_nodes) == 0:
n_no_ncbi += 1
except:
n_no_pub += 1
ident = ''
ncbi_nodes = []
print('{}\t{}\t{}'.format(name, ident, len(ncbi_nodes)))
print('{} drugs'.format(len(uniq)))
print('{} without pubchem id'.format(n_no_pub))
print('{} without genes'.format(n_no_ncbi))
ngood = len(uniq) - n_no_pub - n_no_ncbi
print('{} good ({})'.format(ngood, ngood / len(uniq)))
def test_q2_diseases():
t = TranslatorKnowledgeBeaconAggregator (ServiceContext.create_context ())
n_good = 0
n_bad = 0
diseases = set()
# with open('q2-drugandcondition-list.txt','r') as inf, open('q2_disease_report.txt','w') as outf:
# h = inf.readline()
# outf.write('OriginalName\tDOIDs\tOthers\n')
# for line in inf:
# x = line.strip().split('\t')[1]
with open('q1-disease-list.txt','r') as inf, open('q1_disease_report.txt','w') as outf:
h = inf.readline()
outf.write('OriginalName\tDOIDs\tOthers\n')
for line in inf:
x = line.strip().split('\t')[0]
if x in diseases:
continue
diseases.add(x)
node = KNode("NAME.DISEASE:{}".format(x), node_types.DISEASE)
result = t.name_to_doid (node)
if len(result) == 0:
doids = ''
aliases = t.name_to_anything(node)
n_bad += 1
else:
n_good += 1
doids = ';'.join( [r[1].identifier for r in result] )
aliases = ''
outf.write('{}\t{}\t{}\n'.format(x, doids, aliases))
print( 'Good: {} Bad: {}'.format(n_good, n_bad) )
def __init__(self, config=None, override={}):
self.service_context = ServiceContext.create_context(config)
service_context = self.service_context
self.clinical = Clinical(service_context)
#temporarly taken out because of http errors
#self.exposures = CMAQ (service_context)
self.endotype = Endotype(service_context)
self.chembio = ChemBioKS(self.service_context)
self.chemotext = Chemotext(self.service_context)
self.disease_ontology = DiseaseOntology(self.service_context)
self.pharos = Pharos(self.service_context)
self.oxo = OXO(self.service_context)
self.hpo = HPO(self.service_context)
self.hetio = HetIO(self.service_context)
self.biolink = Biolink(self.service_context)
self.mondo = Mondo(self.service_context)
self.go = GO(self.service_context)
self.tkba = TranslatorKnowledgeBeaconAggregator(self.service_context)
self.translator_registry = TranslatorRegistry(self.service_context)
self.quickgo = QuickGo(self.service_context)
self.translator = Translator(core=self)
self.hgnc = HGNC(self.service_context)
self.uberongraph = UberonGraphKS(self.service_context)
self.ctd = CTD(self.service_context)
def test_all_drugs_ctd():
from greent.service import ServiceContext
ctd = CTD(ServiceContext.create_context())
with open('q2-drugandcondition-list.txt', 'r') as inf:
h = inf.readline()
uniq = set()
for line in inf:
x = line.split('\t')[0]
uniq.add(x)
n_no_ctd = 0
n_no_gene = 0
for name in uniq:
input_node = KNode("DRUG_NAME:{}".format(name),
type=node_types.CHEMICAL_SUBSTANCE_NAME)
results = ctd.drugname_to_ctd(input_node)
try:
drug_node = results[0][1]
ident = drug_node.identifier
except:
n_no_ctd += 1
ident = ''
gene_nodes = []
if ident != '':
gene_nodes = ctd.drug_to_gene(drug_node)
if len(gene_nodes) == 0:
n_no_gene += 1
print('{}\t{}\t{}\t{}'.format(name, ident, len(results),
len(gene_nodes)))
print('{} drugs'.format(len(uniq)))
print('{} without pubchem id'.format(n_no_ctd))
print('{} without genes'.format(n_no_gene))
ngood = len(uniq) - n_no_ctd - n_no_gene
print('{} good ({})'.format(ngood, ngood / len(uniq)))
def test_all_drugs_pharos():
from greent.service import ServiceContext
pharos = Pharos(ServiceContext.create_context())
with open('q2-drugandcondition-list.txt', 'r') as inf:
h = inf.readline()
uniq = set()
for line in inf:
x = line.split('\t')[0]
uniq.add(x)
n_no_pharos = 0
n_no_hgnc = 0
for name in uniq:
input_node = KNode("DRUG_NAME:{}".format(name),
type=node_types.CHEMICAL_SUBSTANCE_NAME)
try:
results = pharos.drugname_to_pharos(input_node)
#print(name, results)
drug_node = results[0][1]
ident = drug_node.identifier
hgnc_nodes = pharos.drug_get_gene(drug_node)
if len(hgnc_nodes) == 0:
n_no_hgnc += 1
except:
# print ('Not finding {}'.format(name))
# exit()
n_no_pharos += 1
ident = ''
hgnc_nodes = []
print('{}\t{}\t{}\t{}'.format(name, ident, len(results),
len(hgnc_nodes)))
print('{} drugs'.format(len(uniq)))
print('{} without pubchem id'.format(n_no_pharos))
print('{} without genes'.format(n_no_hgnc))
ngood = len(uniq) - n_no_pharos - n_no_hgnc
print('{} good ({})'.format(ngood, ngood / len(uniq)))
def test_gc():
b = Biolink(ServiceContext.create_context())
gene = KNode('HGNC:4851', node_type=node_types.GENE)
results = b.gene_get_disease(gene)
for e, k in results:
print(k)
results = b.gene_get_genetic_condition(gene)
print(results)
class TestHetIO(unittest.TestCase):
h = HetIO(ServiceContext.create_context())
def test_anatomy(self):
pprint(self.h.gene_to_anatomy(KNode('HGNC:TP53', node_types.GENE)))
def test_cell(self):
pprint(self.h.gene_to_cell(KNode('HGNC:7121', node_types.GENE)))
def basic_test():
t = TranslatorKnowledgeBeaconAggregator (ServiceContext.create_context ())
#print (t.name_to_mesh_disease (KNode("NAME.DISEASE:asthma", node_types.NAME_DISEASE)))
#print (t.name_to_doid (KNode("NAME.DISEASE:asthma", node_types.DISEASE)))
print ('1.')
print (t.name_to_doid (KNode("NAME.DISEASE:Osteoporosis", node_types.DISEASE)))
print ('2.')
print (t.name_to_doid (KNode("NAME.DISEASE:HIV infection", node_types.DISEASE)))
print ('3.')
print (t.name_to_efo (KNode("NAME.DISEASE:HIV infection", node_types.DISEASE)))
def test():
uk = UberonGraphKS(ServiceContext.create_context())
#Test cell->anatomy
# k = KNode('CL:0000097',node_types.CELL)
# results = uk.get_anatomy_by_cell_graph( k )
# print(results)
#Test pheno->anatomy
k = KNode('HP:0011675', node_types.PHENOTYPE)
results = uk.get_anatomy_by_phenotype_graph(k)
print(results)
def test():
q = QuickGo(ServiceContext.create_context())
r = q.go_term_xontology_relationships(
KNode("GO:0002551", node_types.PROCESS))
pprint.pprint(r)
r = q.go_term_xontology_relationships(
KNode("GO.BIOLOGICAL_PROCESS:0042626", node_types.PROCESS))
pprint.pprint(r)
r = q.go_term_annotation_extensions(
KNode("GO.BIOLOGICAL_PROCESS:0007269", node_types.PROCESS))
pprint.pprint(r)
def __init__(self,greent):
greent.chemotext2 = chemotext2.Chemotext2( ServiceContext.create_context() )
self.chemotext2 = greent.chemotext2
self.badwords = set(['disease','virus','infection','fever','syndrome','hemorrhagic','disorder',\
'gene','cell','system','tissue','non','positive','negative','receptor',\
'type','severe','perinatal','form','adult','onset','nonsyndromic','syndromic',\
'syndrome','infantile','juvenile','early','late','chronic','rare',\
'autosomal','dominant','recessive', 'congenital','hereditary','familial',\
'male','female','with','without','single','mutation','isolated','absence','group', \
'susceptibility','plus','essential','distal','and','during','continuous',\
'due','deficiency','extensive','large','small','pro','partial','complete','morbid', \
'central','distal','middle','deficit','defect','status','rhythm','like'])
def test_pathways():
b = Biolink(ServiceContext.create_context())
gene = KNode('HGNC:5013', node_type=node_types.GENE)
results = b.gene_get_kegg_pathway(gene)
for e, k in results:
print(k)
results = b.gene_get_react_pathway(gene)
for e, k in results:
print(k)
k = results[0][1]
results = b.pathway_get_gene(k)
for e, k in results:
print(k)
def build_synonym_cache(ctext=None):
if ctext is None:
from greent.service import ServiceContext
ctext = Chemotext(ServiceContext.create_context())
response = ctext.query(query="MATCH (d:Term) RETURN d")
with open(ctext.cache, 'w') as outfile:
outfile.write('QUERY\tKEY\n')
res = response['results'][0]
n = 0
for datum in res['data']:
rows = datum['row']
for row in rows:
n += 1
rowtype = row['type']
meshname = row['name']
if 'synonyms' in row:
rowsyn = row['synonyms']
else:
rowsyn = []
outfile.write('{}\t{}\n'.format(meshname.upper(), meshname))
for syn in rowsyn:
outfile.write('{}\t{}\n'.format(syn.upper(), meshname))
def test():
h = HPO (ServiceContext.create_context ())
#print( h.search('Arrhythmias, Cardiac') )
print( h.search('Thyroid Neoplasms') )
print( h.search('Neoplasm of the thyroid gland') )
node_types.PHENOTYPE)))
return edge_node
#return [ ( self.get_edge ({ 'res' : r }, predicate='affects'), KNode("MESH:{0}".format (r['identifier']), 'PH') ) for r in result ]
class TestHetIO(unittest.TestCase):
h = HetIO(ServiceContext.create_context())
def test_anatomy(self):
pprint(self.h.gene_to_anatomy(KNode('HGNC:TP53', node_types.GENE)))
def test_cell(self):
pprint(self.h.gene_to_cell(KNode('HGNC:7121', node_types.GENE)))
if __name__ == '__main__':
het = HetIO(ServiceContext.create_context())
print(het.disease_to_phenotype(KNode('DOID:2841', node_types.DISEASE)))
'''
with open('hgnc-entrez', 'r') as stream:
for line in stream:
h, e, u = line.split ('\t')
het.gene_to_anatomy (KNode('SOMETHING:{}'.format (e), node_types.GENE))
'''
#unittest.main ()
#MATCH (g:Gene)-[r]-(c:CellularComponent) WHERE g.name='HGNC:3263' RETURN g, r, c LIMIT 200
def test():
from greent.service import ServiceContext
cb = ChemBioKS(ServiceContext.create_context())
print(cb.drugname_to_pubchem('imatinib'))
input_node = KNode("DRUG_NAME:imatinib", node_types.DRUG_NAME)
print(cb.graph_drugname_to_pubchem(input_node))
def test():
from greent.service import ServiceContext
hgnc = HGNC( ServiceContext.create_context() )
input_knode = KNode( 'NCBIGENE:3815' , node_type = node_types.GENE )
print( hgnc.ncbigene_to_uniprotkb( input_knode ) )
def test_name():
uk = UberonGraphKS(ServiceContext.create_context())
#Test cell->name
cn = 'CL:0000097'
results = uk.cell_get_cellname(cn)
print(results)
return {
"exposures" : exposures,
"icd_codes" : icd_codes,
"lat" : lat,
"lon" : lon,
"time" : time,
"visit_type" : visit_type
}
def get_endotype (self, request):
print (json.dumps (request))
r = self.client.endotypes.endotypes_post (input=request).result()
print (r)
return r['output'] if 'output' in r else None
if __name__ == "__main__":
e = Endotype (ServiceContext.create_context ())
exposures = list(map(lambda exp : e.create_exposure (**exp), [{
"exposure_type": "pm25",
"units" : "",
"value" : 2
}]))
visits = list(map(lambda v : e.create_visit(**v), [{
"icd_codes" : "ICD9:V12,ICD9:E002",
"lat" : "20",
"lon" : "20",
"time" : "2017-10-12 21:12:29",
"visit_type" : "INPATIENT",
"exposures" : exposures
}]))
request = e.create_request (dob= "2017-10-04", model_type="M0", race="1", sex="M", visits = visits)
print (json.dumps (e.get_endotype (request), indent=2))
rosetta_config = Resource.get_resource_obj("rosetta.yml",
format='yaml')
semantics = rosetta_config['@translator-semantics']
subscriptions = []
for api in self.op_map:
for in_type, in_params in self.op_map[api].items():
for out_type, out_vals in self.op_map[api][in_type].items():
predicate = semantics.get(api,
{}).get(in_type,
{}).get(out_type, None)
#if not predicate:
# predicate = '*-missing-*'
subscriptions.append((in_type, out_type, {
"link":
predicate,
"op":
self.add_method(TranslatorRegistry, api, in_type,
out_type)
}))
return subscriptions
if __name__ == "__main__":
""" Load the registry """
treg = TranslatorRegistry(ServiceContext.create_context())
""" Generate subscriptions """
subscriptions = treg.get_subscriptions()
r = treg.myvariantinfo__uniprot_to_hgvs(
KNode('UNIPROT:AKT1', node_types.GENE))
pprint(r)
def __init__(self, context):
super(Biolink, self).__init__("biolink", context)
# TODO, can we just use the Mondo that's inthe core already?
self.checker = Mondo(ServiceContext.create_context())
self.go = GO(ServiceContext.create_context())
def test_go():
KIT_protein = KNode('UniProtKB:P10721', node_types.GENE)
b = Biolink(ServiceContext.create_context())
results = b.gene_get_process(KIT_protein)
for ke, kn in results:
print(ke, kn)
def __init__(self):
""" Create a GreenT service context. Initialize the Chemotext service with that context."""
self.service_context = ServiceContext()
self.chemotext = Chemotext(self.service_context)
def test_phenotypes():
asthma = KNode('DOID:2841', node_types.DISEASE)
b = Biolink(ServiceContext.create_context())
results = b.disease_get_phenotype(asthma)
for ke, kn in results:
print(ke, kn)
def test_parts():
uk = UberonGraphKS(ServiceContext.create_context())
print(uk.get_anatomy_parts('UBERON:0004535'))
raise ValueError ("We don't have a word embedding model for {0} word phrases".format (term_a.count(' ') + 1))
model = self.bigram_model
term_a = term_a.replace (' ', '_')
term_b = term_b.replace (' ', '_')
elif term_a.count (' ') == 0:
model = self.model
else:
raise ValueError ("We don't have a word embedding model for {0} word phrases".format (term_a.count(' ') + 1))
return model.similarity (term_a, term_b) if term_a in model.vocab and term_b in model.vocab else -1.0
#return self.model.similarity (term_a, term_b) if term_a in self.model.vocab and term_b in self.model.vocab else -1.0
if __name__ == "__main__":
ct2 = Chemotext2 (ServiceContext.create_context ())
print (ct2.get_semantic_similarity ("lung cancer", "p53"))
print (ct2.get_semantic_similarity ("cell line", "disease"))
print (ct2.get_semantic_similarity ("cellular component", "nucleus"))
print (ct2.get_semantic_similarity ("cell cycle", "krebbs"))
print (ct2.get_semantic_similarity ("MAPK2", "P53"))
w = [ "albuterol", "imatinib", "aspirin", "atrovent", "decadron", "medrol", "rayos" , "abemaciclib", "abraxane"]
pairs = zip (w, w[1:])
for k, v in pairs:
print ((" k %s -> v %s : sim: %s" % (k, v, ct2.get_semantic_similarity (k, v))))
print (ct2.model.most_similar (positive=['aspirin' ]))
print (ct2.model.most_similar (positive=['p53' ]))
print (ct2.model.most_similar (positive=['kit' ]))
print (ct2.model.most_similar (positive=['asthma' ]))
def test():
m = Mondo(ServiceContext.create_context())
huntington = KNode('OMIM:143100', node_types.DISEASE)
print(m.is_genetic_disease(huntington))
print('------')
