def get_given_drugs_related_info(disease_pairs, drugs): # first disease pair with first drug array
all_networks = [] # contains an array for each disease pair
for index, disease_pair in enumerate(disease_pairs):
networks_per_drug = [] # contains a network for each drug
pair_drugs_ids = drugs[index]
temp_id1 = disease_pair[0].replace(':', '-')
temp_id2 = disease_pair[1].replace(':', '-')
path = '../analysis/disease_pairs/' + temp_id1 + '_' + temp_id2
for drug_id in pair_drugs_ids:
try:
os.mkdir(path)
except FileExistsError:
pass
network = Network()
d1 = Disease([disease_pair[0]], [])
network.add_node(d1)
d2 = Disease([disease_pair[1]], [])
network.add_node(d2)
drug = Drug([drug_id], [])
network.add_node(drug)
temp_drug_id = drug_id.replace(':', '-')
with io.open(path + '/' + temp_id1 + '_' + temp_id2 + '_' + temp_drug_id + '_results.txt', 'w', encoding='utf-8', newline='') as results_file:
results_file.write('In this file all information about the connection between ' + disease_pair[0] +
' and ' + disease_pair[1] + ' and the drug ' + drug_id + ' is summarized:\n')
# the drug INDICATES, CONTRAINDICATES or INDUCES the disease
query = """ MATCH (d:Disease)-[a]-(n:Drug) WHERE {d1_id} IN d.ids AND {n_id} in n.ids RETURN distinct(type(a)) """
d1_results = session.run(query, parameters={'d1_id': disease_pair[0], 'n_id': drug_id})
for result in d1_results:
results_file.write(drug_id + ' ' + result['(type(a))'] + ' ' + disease_pair[0] + '\n')
network.add_edge(Edge(drug, d1, result['(type(a))'], {}))
query = """ MATCH (d:Disease)-[a]-(n:Drug) WHERE {d2_id} IN d.ids AND {n_id} in n.ids RETURN distinct(type(a)) """
d2_results = session.run(query, parameters={'d2_id': disease_pair[1], 'n_id': drug_id})
for result in d2_results:
results_file.write(drug_id + ' ' + result['(type(a))'] + ' ' + disease_pair[1] + '\n')
network.add_edge(Edge(drug, d2, result['(type(a))'], {}))
# the drug targets a gene which is associated to the disease
d1_genes = set()
query = """ MATCH (n:Drug)-[:TARGETS]-(g:Gene)-[:ASSOCIATES_WITH]-(d:Disease) WHERE {d1_id} IN d.ids AND {n_id} in n.ids RETURN g.`_id` """
d1_results = session.run(query, parameters={'d1_id': disease_pair[0], 'n_id': drug_id})
for gene in d1_results:
d1_genes.add(gene['g.`_id`'])
g = Gene([gene['g.`_id`']], [])
network.add_node(g)
network.add_edge(Edge(drug, g, 'TARGETS', {'actions': []})) #TODO
network.add_edge(Edge(g, d1, 'ASSOCIATES_WITH', {}))
d2_genes = set()
query = """ MATCH (n:Drug)-[:TARGETS]-(g:Gene)-[:ASSOCIATES_WITH]-(d:Disease) WHERE {d2_id} IN d.ids AND {n_id} in n.ids RETURN g.`_id` """
d2_results = session.run(query, parameters={'d2_id': disease_pair[1], 'n_id': drug_id})
for gene in d2_results:
d2_genes.add(gene['g.`_id`'])
g = Gene([gene['g.`_id`']], [])
network.add_node(g)
network.add_edge(Edge(drug, g, 'TARGETS', {'actions': []})) #TODO
network.add_edge(Edge(g, d2, 'ASSOCIATES_WITH', {}))
common_drug_genes = d1_genes.intersection(d2_genes) # genes associated to the drug and both diseases
# relevant_genes are all genes associated to at least one disease and the drug, below the common genes
# with the most disease associated references are added
relevant_genes = d1_genes.union(d2_genes)
if len(d1_genes) > 0:
nbr = str(len(d1_genes))
d1_genes = str(d1_genes)
d1_genes = d1_genes.replace('{', '')
d1_genes = d1_genes.replace('}', '')
d1_genes = d1_genes.replace('\'', '')
results_file.write(drug_id + ' targets following ' + nbr + ' genes which are associated to ' + disease_pair[0] + ': ' + d1_genes + '\n')
if len(d2_genes) > 0:
nbr = str(len(d2_genes))
d2_genes = str(d2_genes)
d2_genes = d2_genes.replace('{', '')
d2_genes = d2_genes.replace('}', '')
d2_genes = d2_genes.replace('\'', '')
results_file.write(drug_id + ' targets following ' + nbr + ' genes which are associated to ' + disease_pair[1] + ': ' + d2_genes + '\n')
if len(common_drug_genes) > 0:
nbr = str(len(common_drug_genes))
cdgs = str(common_drug_genes)
cdgs = cdgs.replace('{', '')
cdgs = cdgs.replace('}', '')
cdgs = cdgs.replace('\'', '')
results_file.write('The disease pair has ' + nbr + ' common genes which are targeted by the drug: ' + cdgs + '\n')
# add the common genes with the most disease associated references
# no given num_pmids is similar to num_pmids = 0
all_d1_genes, all_d2_genes = get_genes(disease_pair)
all_common_genes = all_d1_genes.intersection(all_d2_genes)
relevant_common_genes = [] # the genes with the most cited gene-disease association, threshold 10
if len(all_common_genes) > 0:
results_file.write('The disease pair has ' + str(len(all_common_genes)) + ' common genes, not considering the connection to the drug.'
' Following genes have the most references regarding their connection to both diseases:\n')
for gene in all_common_genes:
query = """ MATCH (d1:Disease)-[a]-(g:Gene) WHERE {g_id} IN g.ids AND {d1_id} IN d1.ids RETURN a.num_pmids """
results = session.run(query, parameters={'g_id': gene, 'd1_id': disease_pair[0]})
num_pmids = 0
for result in results: # multiple edges to the same gene
temp = result['a.num_pmids']
if temp is not None:
num_pmids = num_pmids + temp
query = """ MATCH (d2:Disease)-[a]-(g:Gene) WHERE {g_id} IN g.ids AND {d2_id} IN d2.ids RETURN a.num_pmids """
results = session.run(query, parameters={'g_id': gene, 'd2_id': disease_pair[1]})
for result in results: # multiple edges to the same gene
temp = result['a.num_pmids']
if temp is not None:
num_pmids = num_pmids + temp
relevant_common_genes.append([gene, num_pmids])
# sort by number of pmids
relevant_common_genes = sorted(relevant_common_genes, key=lambda item: item[1], reverse=True)
relevant_common_genes = relevant_common_genes[:10] # threshold
rcgs = str(relevant_common_genes)
rcgs = rcgs[1:-1]
rcgs = rcgs.replace('\'', '')
results_file.write(rcgs + '\n')
for g in relevant_common_genes:
gene = Gene([g[0]], [])
network.add_node(gene)
network.add_edge(Edge(gene, d1, 'ASSOCIATES_WITH', {}))
network.add_edge(Edge(gene, d2, 'ASSOCIATES_WITH', {}))
relevant_genes.add(g[0])
# add the common disease associated variants with most references
# no given num_pmids is similar to num_pmids = 0
disease_variants = {}
query = """ MATCH (d1:Disease)-[a]-(v:Variant)--(d2:Disease) WHERE {d1_id} in d1.ids AND {d2_id} in d2.ids RETURN distinct(a.num_pmids), v.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for variant in results:
num_pmids = variant['(a.num_pmids)']
if num_pmids is None:
num_pmids = 0
var_id = variant['v.`_id`']
if var_id in disease_variants:
temp = disease_variants[var_id]
disease_variants[var_id] = temp + num_pmids
else:
disease_variants[var_id] = num_pmids
query = """ MATCH (d2:Disease)-[a]-(v:Variant)--(d1:Disease) WHERE {d1_id} in d1.ids AND {d2_id} in d2.ids RETURN distinct(a.num_pmids), v.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for variant in results:
num_pmids = variant['(a.num_pmids)']
if num_pmids is None:
num_pmids = 0
var_id = variant['v.`_id`']
if var_id in disease_variants:
temp = disease_variants[var_id]
disease_variants[var_id] = temp + num_pmids
else:
disease_variants[var_id] = num_pmids
dvs = ''
i = 0
for key, value in sorted(disease_variants.items(), key=lambda item: item[1], reverse=True):
if i < 9: # threshold
num_pmids = disease_variants[key]
variant = Variant([key], [])
network.add_node(variant)
network.add_edge(Edge(variant, d1, 'ASSOCIATES_WITH', {}))
network.add_edge(Edge(variant, d2, 'ASSOCIATES_WITH', {}))
dvs = dvs + key + ':' + str(num_pmids) + ' PMIDs, '
i += 1
dvs = dvs[:-2]
# add the gene associated variants with smallest pvalues
# if no pvalue is given, pvalue is set to 1
gene_variants = []
for gene in relevant_genes:
query = """ MATCH (g:Gene)-[a]-(v:Variant) WHERE {g_id} in g.ids RETURN v.`_id`, a.pvalue, type(a) """
results = session.run(query, parameters={'g_id': gene})
for variant in results:
pvalue = variant['a.pvalue']
if pvalue is None:
pvalue = 1
else:
pvalue = float(pvalue)
gene_variants.append([variant['v.`_id`'] + '-' + gene, pvalue, variant['type(a)']])
gene_variants = sorted(gene_variants, key=lambda item: item[1])
gene_variants = gene_variants[:10] # threshold
for v in gene_variants:
temp = v[0].split('-')
v_id = temp[0]
g_id = temp[1]
variant = Variant([v_id], [])
network.add_node(variant)
gene = Gene([g_id], [])
network.add_node(gene)
network.add_edge(Edge(gene, variant, v[2], {'pvalue': v[1]}))
if len(gene_variants) > 0:
gvs = str(gene_variants)
gvs = gvs[1:-1]
gvs = gvs.replace('\'', '')
else:
gvs = ''
if len(disease_variants) > 0 or len(gene_variants) > 0:
results_file.write('The disease pair has at least ' + str(i) + ' variants associated to both diseases: ' +
dvs + ' and at least ' + str(len(gene_variants)) + ' gene associated variants: ' + gvs + '\n')
# dict with RNA name as key and an array as value
# first array position is the number of regulated genes, second position is an array with the gene names
relevant_rnas = {}
for gene in relevant_genes:
query = """ MATCH (r:RNA)--(g:Gene) WHERE {g_id} in g.ids AND NOT r.label_id CONTAINS "MRNA" return r.`_id` """
results = session.run(query, parameters={'g_id': gene})
for result in results:
key = result['r.`_id`']
if key in relevant_rnas:
value = relevant_rnas[key]
genes = value[1]
if gene not in genes:
genes.add(gene)
relevant_rnas[key] = [value[0] + 1, genes]
else:
genes = set()
genes.add(gene)
relevant_rnas[key] = [1, genes]
if len(relevant_rnas) > 0:
i = 0
for key, value in sorted(relevant_rnas.items(), key=lambda item: item[1], reverse=True):
# sort by the number of regulated genes
if i > 9: # threshold
break
elif value[0] > 1: # only add and print RNAs which regulate more than one gene
if i == 0:
results_file.write('RNAs with the number and names of the genes they regulate: \n')
rna_id = key
for gene_id in value[1]:
rna = RNA([rna_id], [])
network.add_node(rna)
gene = Gene([gene_id], [])
network.add_node(gene)
network.add_edge(Edge(rna, gene, 'REGULATES', {}))
regulated_genes = str(value[1])
regulated_genes = regulated_genes[1:-1]
regulated_genes = regulated_genes.replace('\'', '')
results_file.write(rna_id + '\t' + str(value[0]) + '\t' + regulated_genes + '\n')
i += 1
# append regulating RNAs to one RNA which regulates the most genes, MRNAs are not added
for key, value in sorted(relevant_rnas.items(), key=lambda item: item[1], reverse=True):
if value[0] > 1:
most_relevant_rna = RNA([key], [])
network.add_node(most_relevant_rna)
query = """ MATCH (r:RNA)--(n:RNA) WHERE {r_id} in r.ids AND NOT n.label_id CONTAINS "MRNA" RETURN n.`_id`, labels(n) """
results = session.run(query, parameters={'r_id': key})
reg_rnas = ''
for result in results:
rna_id = result['n.`_id`']
types = result['labels(n)']
for type in types:
if type != 'RNA':
if type == 'CircRNA':
rna = CircRNA([rna_id], [])
if type == 'ERNA':
rna = ERNA([rna_id], [])
if type == 'LncRNA':
rna = LncRNA([rna_id], [])
if type == 'MiRNA':
rna = MiRNA([rna_id], [])
if type == 'NcRNA':
rna = NcRNA([rna_id], [])
if type == 'PiRNA':
rna = PiRNA([rna_id], [])
if type == 'Pseudogene':
rna = Pseudogene([rna_id], [])
if type == 'Ribozyme':
rna = Ribozyme([rna_id], [])
if type == 'RRNA':
rna = RRNA([rna_id], [])
if type == 'ScaRNA':
rna = ScaRNA([rna_id], [])
if type == 'ScRNA':
rna = ScRNA([rna_id], [])
if type == 'SnoRNA':
rna = SnoRNA([rna_id], [])
if type == 'SnRNA':
rna = SnRNA([rna_id], [])
network.add_node(rna)
network.add_edge(Edge(rna, most_relevant_rna, 'REGULATES', {}))
reg_rnas = reg_rnas + rna_id + ', '
reg_rnas = reg_rnas[:-2]
results_file.write(key + ' is the RNA which regulates the most genes in this subgraph. It is regulated by ' + reg_rnas + '.\n')
break
json_file = path + '/' + temp_id1 + '_' + temp_id2 + '_' + temp_drug_id + '_graph.json'
network.save(json_file)
draw_drug_subgraph(json_file)
networks_per_drug.append(network)
all_networks.append(networks_per_drug)
return all_networks
network = Network()
drug_lookup = {}
with io.open(drug_file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
for row in reader:
drug_lookup[row[0].strip()] = row[1].strip()
# 1: STITCH compound id (flat, see above)
# 2: UMLS concept id as it was found on the label
# 3: method of detection: NLP_indication / NLP_precondition / text_mention
# 4: concept name
# 5: MedDRA concept type (LLT = lowest level term, PT = preferred term; in a few cases the term is neither LLT nor PT)
# 6: UMLS concept id for MedDRA term
# 7: MedDRA concept name
# All side effects found on the labels are given as LLT. Additionally, the PT is shown. There is at least one
# PT for every LLT, but sometimes the PT is the same as the LLT.
with io.open(file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
for row in reader:
pubchem_id = row[0][4::].lstrip('0')
drug = Drug(['PubChem:CID%s' % pubchem_id], [drug_lookup[row[0]]] if row[0] in drug_lookup else [])
network.add_node(drug)
disease = Disease(['UMLS:%s' % row[1], 'UMLS:%s' % row[5]], [row[3], row[6]])
network.add_node(disease)
network.add_edge(Edge(drug, disease, 'INDICATES', {'source': 'SIDER'}))
network.save('../data/SIDER/graph.json')
#!/usr/bin/env python3
import io
import csv
from model.network import Network
from model.drug import Drug
from model.disease import Disease
from model.edge import Edge
network = Network()
with io.open('../data/PubMed/drug_disease.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
drug = Drug(['DrugBank:%s' % row[1]], [row[0]])
disease = Disease([row[4]], [row[3]])
network.add_node(drug)
network.add_node(disease)
network.add_edge(
Edge(drug, disease, row[2], {
'source': 'PubMed',
'pmid': row[5]
}))
network.save('../data/PubMed/graph.json')
def get_common_drugs(disease_pairs, networks, writing_files):
new_networks = []
for index, disease_pair in enumerate(disease_pairs):
network = networks[index]
d1 = Disease([disease_pair[0]], [])
network.add_node(d1)
d2 = Disease([disease_pair[1]], [])
network.add_node(d2)
# the drug INDICATES, CONTRAINDICATES or INDUCES both diseases
common_drugs = set()
query = """ MATCH (d1:Disease)-[a]-(n:Drug)--(d2:Disease) WHERE {d1_id} IN d1.ids AND {d2_id} IN d2.ids RETURN distinct(type(a)), n.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for result in results:
drug_id = result['n.`_id`']
type = result['(type(a))']
common_drugs.add(drug_id)
drug = Drug([drug_id], [])
network.add_node(drug)
network.add_edge(Edge(drug, d1, type, {}))
query = """ MATCH (d1:Disease)--(n:Drug)-[a]-(d2:Disease) WHERE {d1_id} IN d1.ids AND {d2_id} IN d2.ids RETURN distinct(type(a)), n.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for result in results:
drug_id = result['n.`_id`']
type = result['(type(a))']
common_drugs.add(drug_id)
drug = Drug([drug_id], [])
network.add_node(drug)
network.add_edge(Edge(drug, d2, type, {}))
# the drug targets a gene of one disease and is associated to the other disease
query = """ MATCH (d1:Disease)-[a]-(n:Drug)-[:TARGETS]-(g:Gene)-[:ASSOCIATES_WITH]-(d2:Disease) WHERE {d1_id} IN d1.ids AND {d2_id} IN d2.ids RETURN distinct(type(a)), n.`_id`, g.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for result in results:
drug_id = result['n.`_id`']
type = result['(type(a))']
common_drugs.add(drug_id)
drug = Drug([drug_id], [])
network.add_node(drug)
network.add_edge(Edge(drug, d1, type, {}))
gene_id = result['g.`_id`']
gene = Gene([gene_id], [])
network.add_node(gene)
network.add_edge(Edge(drug, gene, 'TARGETS', {'actions': []}))
network.add_edge(Edge(gene, d2, 'ASSOCIATES_WITH', {}))
query = """ MATCH (d2:Disease)-[a]-(n:Drug)-[:TARGETS]-(g:Gene)-[:ASSOCIATES_WITH]-(d1:Disease) WHERE {d1_id} IN d1.ids AND {d2_id} IN d2.ids RETURN distinct(type(a)), n.`_id`, g.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for result in results:
drug_id = result['n.`_id`']
type = result['(type(a))']
common_drugs.add(drug_id)
drug = Drug([drug_id], [])
network.add_node(drug)
network.add_edge(Edge(drug, d2, type, {}))
gene_id = result['g.`_id`']
gene = Gene([gene_id], [])
network.add_node(gene)
network.add_edge(Edge(drug, gene, 'TARGETS', {'actions': []}))
network.add_edge(Edge(gene, d1, 'ASSOCIATES_WITH', {}))
# the drug targets one gene which is associated to both diseases or the drug targets two different genes
# where each gene is associated to one of the diseases
query = """ MATCH (d1:Disease)-[:ASSOCIATES_WITH]-(g1:Gene)-[:TARGETS]-(n:Drug)-[:TARGETS]-(g2:Gene)-
[:ASSOCIATES_WITH]-(d2:Disease) WHERE {d1_id} IN d1.ids AND {d2_id} IN d2.ids RETURN n.`_id`, g1.`_id`, g2.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for result in results:
drug_id = result['n.`_id`']
common_drugs.add(drug_id)
g1_id = result['g1.`_id`']
g2_id = result['g2.`_id`']
g1 = Gene([g1_id], [])
network.add_node(g1)
network.add_edge(Edge(g1, d1, 'ASSOCIATES_WITH', {}))
drug = Drug([drug_id], [])
network.add_node(drug)
network.add_edge(Edge(drug, g1, 'TARGETS', {'actions': []}))
g2 = Gene([g2_id], [])
network.add_node(g2)
network.add_edge(Edge(drug, g2, 'TARGETS', {'actions': []}))
network.add_edge(Edge(g2, d2, 'ASSOCIATES_WITH', {}))
new_networks.append(network)
if len(common_drugs) > 0 and writing_files:
temp_id1 = disease_pair[0].replace(':', '-')
temp_id2 = disease_pair[1].replace(':', '-')
path = '../analysis/disease_pairs/' + temp_id1 + '_' + temp_id2
try:
os.mkdir(path)
except FileExistsError:
pass
with io.open(path + '/' + temp_id1 + '_' + temp_id2 + '_common_drugs.tsv', 'w', encoding='utf-8', newline='') as common_drugs_file:
common_drugs_file.write('#Common drugs of ' + disease_pair[0] + ' and ' + disease_pair[1] + '\n')
for drug in common_drugs:
common_drugs_file.write(drug + '\n')
print('Done getting drugs')
return new_networks
if concept['kind'] == kind_defs_rev['DRUG_KIND']:
drug_ids = ['NDFRT:%s' % concept['code']]
drug_names = [concept['name']]
for prop in concept['properties']:
if property_defs[prop[0]] == 'RxNorm_CUI':
drug_ids.append('RxNorm:%s' % prop[1])
elif property_defs[prop[0]] == 'RxNorm_Name':
drug_names.append(prop[1])
elif property_defs[prop[0]] == 'UMLS_CUI':
drug_ids.append('UMLS:%s' % prop[1])
elif property_defs[prop[0]] == 'Synonym':
drug_names.append(prop[1])
drug_names = [
x.replace('[VA Product]', '').strip() for x in drug_names
]
drug = Drug(drug_ids, drug_names)
network.add_node(drug)
for role in concept['roles']:
role_name = role_defs[role[0]]
rel = {'source': 'NDFRT'}
if role_name == 'induces {NDFRT}':
network.add_edge(
Edge(drug, ('NDFRT:%s' % role[1], 'Disease'), 'INDUCES',
rel))
elif role_name == 'CI_with {NDFRT}':
network.add_edge(
Edge(drug, ('NDFRT:%s' % role[1], 'Disease'),
'CONTRAINDICATES', rel))
elif role_name == 'may_treat {NDFRT}':
network.add_edge(
Edge(drug, ('NDFRT:%s' % role[1], 'Disease'), 'INDICATES',
network = Network()
with io.open(file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
for row in reader:
row = [x.strip() for x in row]
if not row[0] or not row[7] or not row[8]:
continue
gene_ids = {'HGNC:%s' % row[0]}
if row[2]:
gene_ids.add('Entrez:%s' % row[2])
gene = Gene(gene_ids, [])
network.add_node(gene)
drug_name = row[7].replace('(%s)' % row[8], '').replace(row[8],
'').strip()
drug = Drug(['ChEMBL:%s' % row[8]], [drug_name] if drug_name else [])
network.add_node(drug)
rel = {
'source': 'DGIdb,%s' % row[3],
'actions': [row[4]],
}
if row[9]:
pubmed_ids = ','.join(
['PMID:%s' % x for x in row[9].strip().split(',')])
rel['source'] += ',%s' % pubmed_ids
network.add_edge(Edge(drug, gene, 'TARGETS', rel))
network.save('../data/DGIdb/graph.json')
with open_file_in_zip('../data/PharmGKB/drugs.zip', 'drugs.tsv') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
for row in reader:
# Only parse drugs and not drug classes for now
if row[5] == 'Drug':
drug_ids = {'PharmGKB:%s' % row[0]}
drug_ids.update(process_drug_cross_references(split_list(row[6])))
for rx_norm_id in split_list(row[21]):
drug_ids.add('RxNorm:%s' % rx_norm_id)
for atc_code in split_list(row[22]):
drug_ids.add('AtcCode:%s' % atc_code)
for compound_id in split_list(row[23]):
drug_ids.add('PubChem:CID%s' % compound_id)
drug = Drug(drug_ids, [row[1]])
drug.attributes['type'] = row[5]
network.add_node(drug)
with open_file_in_zip('../data/PharmGKB/genes.zip', 'genes.tsv') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
# 0 - PharmGKB Accession Id
# 1 - NCBI Gene ID
# 2 - HGNC ID
# 3 - Ensembl Id
# 4 - Name
# 5 - Symbol
# 6 - Alternate Names
# 7 - Alternate Symbols
# 8 - Is VIP
added_mesh_diseases = set()
for association in root.findall('association'):
association_type = association.find('name').text
from_namespace = association.find('from_namespace').text
from_id = association.find('from_code').text
from_name = association.find('from_name').text
to_namespace = association.find('to_namespace').text
to_id = association.find('to_code').text
to_name = association.find('to_name').text
if from_namespace != 'RxNorm' or to_namespace != 'MeSH':
continue
if association_type not in ['induces', 'CI_with', 'may_treat']:
continue
drug_id = 'RxNorm:%s' % from_id
if from_id not in added_rxnorm_drugs:
drug = Drug([drug_id], [from_name])
network.add_node(drug)
added_rxnorm_drugs.add(from_id)
else:
drug = network.get_node_by_id(drug_id, 'Drug')
disease_id = 'MeSH:%s' % to_id
if to_id not in added_mesh_diseases:
disease = Disease([disease_id], [to_name])
network.add_node(disease)
added_mesh_diseases.add(to_id)
else:
disease = network.get_node_by_id(disease_id, 'Disease')
rel = {'source': 'MEDRT'}
if association_type == 'induces':
network.add_edge(Edge(drug, disease, 'INDUCES', rel))
elif association_type == 'CI_with':
with io.open(snp_adr_output_file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
snp_adrs_results.append(row)
external_id_lookup = {}
for row in external_id_results:
external_id_lookup[row[0]] = [x for x in row[1::] if x]
network = Network()
for row in targets_results:
drug_ids = ['DrugBank:%s' % row[0]]
if row[0] in external_id_lookup:
drug_ids.extend(external_id_lookup[row[0]])
drug = Drug(drug_ids, [row[1]])
network.add_node(drug)
gene_ids = ['HGNC:%s' % row[2]]
if row[4]:
gene_ids.append(row[4])
gene = Gene(gene_ids, [row[3]])
network.add_node(gene)
rel = {
'source': 'DrugBank',
'known_action': row[5] == 1,
'actions': row[6].split(',') if row[6] else [],
'simplified_action': row[7]
}
network.add_edge(Edge(drug, gene, 'TARGETS', rel))
for row in interactions_results:
drug1 = Drug(['DrugBank:%s' % row[0]], [row[1]])
next(reader, None)
for row in reader:
# 0 SUPERDRUG_ID
# 1 PREFERRED_NAME
# 2 ATC
# 3 CHEMBL_ID
# 4 DRUGBANK_ID
# 5 KEGG_ID
# 6 PUBCHEM_CID
# 7 CASRN
drug_ids = [] # ['SuperDrug:%s' % row[0]]
# if row[2] != not_available_text:
# drug_ids.extend(['AtcCode:%s' % x for x in row[2].split(';')])
if row[3] != not_available_text:
for chembl_id in {x.strip() for x in row[3].split(';')}:
drug_ids.append('ChEMBL:%s' % chembl_id)
if row[4] != not_available_text:
drug_ids.append('DrugBank:%s' % row[4])
else:
# For now, only use mappings including DrugBank
continue
# if row[5] != not_available_text:
# drug_ids.append('Kegg:%s' % row[5])
if row[6] != not_available_text:
for pubchem_id in {x.strip() for x in row[6].split(';')}:
drug_ids.append('PubChem:CID%s' % pubchem_id)
if len(drug_ids) > 1:
network.add_node(Drug(drug_ids, [row[1]]))
network.save('../data/SuperDrug2/graph.json')
from model.drug import Drug
from model.disease import Disease
from model.edge import Edge
network = Network()
with io.open('../data/DrugCentral/drugcentral_mappings.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
ids = ['DrugCentral:%s' % row[0], 'DrugBank:%s' % row[1]]
if row[2]:
ids.append('RxNorm:%s' % row[2])
network.add_node(Drug(ids, [row[3]]))
with io.open('../data/DrugCentral/drugcentral_indications.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
disease = Disease(['SnoMedCT:%s' % row[2],
'UMLS:%s' % row[3]], [row[1]])
network.add_node(disease)
drug = network.get_node_by_id('DrugBank:%s' % row[0], 'Drug')
e = Edge(drug, disease, 'INDICATES', {'source': 'DrugCentral'})
network.add_edge(e)