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Parser for DrugMechDB Protein/Gene-[biolink:target_for]->DiseaseOrPhe…
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…notypicFeature edges.
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@beaslejt
beaslejt committed Sep 6, 2023
1 parent b6f6aa8 commit f979316
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4 changes: 4 additions & 0 deletions Common/data_sources.py
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CORD19 = 'Cord19'
CTD = 'CTD'
DRUG_CENTRAL = 'DrugCentral'
DRUGMECHDB = 'DrugMechDB'
# FOODB = 'FooDB' # this is on hold, data needs review after latest release of data.
GENOME_ALLIANCE_ORTHOLOGS = 'GenomeAllianceOrthologs'
GTEX = 'GTEx'
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PANTHER = 'PANTHER'
PHAROS = 'PHAROS'
PLANT_GOA = 'PlantGOA'
REACTOME = "Reactome"
SCENT = 'Scent'
SGD = 'SGD'
HUMAN_STRING = 'STRING-DB-Human'
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CORD19: ("parsers.cord19.src.loadCord19", "Cord19Loader"),
CTD: ("parsers.CTD.src.loadCTD", "CTDLoader"),
DRUG_CENTRAL: ("parsers.drugcentral.src.loaddrugcentral", "DrugCentralLoader"),
DRUGMECHDB: ("parsers.drugmechdb.src.loadDrugMechDB", "DrugMechDBLoader"),
GENOME_ALLIANCE_ORTHOLOGS: ("parsers.GenomeAlliance.src.loadGenomeAlliance", "GenomeAllianceOrthologLoader"),
GTEX: ("parsers.GTEx.src.loadGTEx", "GTExLoader"),
GTOPDB: ("parsers.gtopdb.src.loadGtoPdb", "GtoPdbLoader"),
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PANTHER: ("parsers.panther.src.loadPanther", "PLoader"),
PHAROS: ("parsers.PHAROS.src.loadPHAROS", "PHAROSLoader"),
PLANT_GOA: ("parsers.GOA.src.loadGOA", "PlantGOALoader"),
REACTOME: ("parsers.Reactome.src.loadReactome", "ReactomeLoader"),
SCENT: ("parsers.scent.src.loadScent", "ScentLoader"),
SGD: ("parsers.SGD.src.loadSGD", "SGDLoader"),
TEXT_MINING_KP: ("parsers.textminingkp.src.loadTMKP", "TMKPLoader"),
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168 changes: 168 additions & 0 deletions parsers/drugmechdb/src/loadDrugMechDB.py
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import json
import requests as rq
import os
import pandas as pd

from Common.utils import GetData
from Common.loader_interface import SourceDataLoader
from Common.extractor import Extractor

def load_json(json_data):
with open(json_data, encoding="utf-8") as file:
data = json.load(file)
file.close()
return data

# # Example usage
# json_file = 'indication_paths.json'
# csv_file = 'indication_paths.csv'
# data = load_json(json_file)

##############
# Class: Load in direct Gene/Protein-[biolink:target_for]->Disease relationships from DrugMechDB
# By: Jon-Michael Beasley
# Date: 09/06/2023
##############
class DrugMechDBLoader(SourceDataLoader):

source_id: str = 'DrugMechDB'
provenance_id: str = 'infores:drugmechdb'
description = "A database of paths that represent the mechanism of action from a drug to a disease in an indication."
source_data_url = "https://github.com/SuLab/DrugMechDB/raw/main/indication_paths.json"
license = "SuLab/DrugMechDB is licensed under the Creative Commons Zero v1.0 Universal license"
attribution = 'https://sulab.github.io/DrugMechDB/'
parsing_version = '1.1'

def __init__(self, test_mode: bool = False, source_data_dir: str = None):
"""
constructor
:param test_mode - sets the run into test mode
"""
# call the super
super().__init__(test_mode=test_mode, source_data_dir=source_data_dir)
self.drugmechdb_version = '202307' # TODO temporarily hard coded
#self.drugmechdb_version = self.get_latest_source_version()
self.bindingdb_data_url = [f"https://github.com/SuLab/DrugMechDB/raw/main/"]
self.drugmechdb_file_name = f"indication_paths.json"
self.data_files = [self.drugmechdb_file_name]

#TODO Write the function below to get latest update version from https://sulab.github.io/DrugMechDB/
def get_latest_source_version(self) -> str:
"""
gets the latest version of the data
:return:
"""
if self.bindingdb_version:
return self.bindingdb_version
### The method below gets the database version from the html, but this may be subject to change. ###
binding_db_download_page_response = rq.get('https://www.bindingdb.org/rwd/bind/chemsearch/marvin/Download.jsp')
version_index = binding_db_download_page_response.text.index('BindingDB_All_2D_') + 17
bindingdb_version = binding_db_download_page_response.text[version_index:version_index + 6]

return f"{bindingdb_version}"

def get_data(self) -> int:
"""
Gets the DrugMechDB data.
"""
data_puller = GetData()
i=0
for source in self.data_files:
source_url = f"{self.bindingdb_data_url[i]}{source}"
data_puller.pull_via_http(source_url, self.data_path)
i+=1
return True
def parse_data(self) -> dict:
"""
Parses the data file for graph nodes/edges
:return: ret_val: load_metadata
"""
triple_pair_dict = {
"dmdb_ids":[],
"drug_names":[],
"drug_meshs":[],
"drug_drugbanks":[],
"drug_target_names":[],
"drug_target_uniprots":[],
"disease_names":[],
"disease_meshs":[]
}

data = load_json(os.path.join(self.data_path,self.drugmechdb_file_name))
for entry in data:
dmdb_id = entry["graph"]["_id"]
drug_name = entry["graph"]["drug"]
drug_mesh = entry["graph"]["drug_mesh"]
drug_drugbank = entry["graph"]["drugbank"]
disease_name = entry["graph"]["disease"]
disease_mesh = entry["graph"]["disease_mesh"]
links = entry["links"]

for i in range(len(links)):
triple = links[i]
if triple["source"] == drug_mesh:
source = triple["source"]
predicate = "biolink:" + triple["key"].replace(" ","_")
target = triple["target"]

nodes = entry["nodes"]
for node in nodes:
if (node["id"] == target) and (node["label"] == "Protein"):

drug_target_name = node["name"]
drug_target_uniprot = node["id"].replace('UniProt:', 'UniProtKB:')

triple_pair_dict["dmdb_ids"].append(dmdb_id)
triple_pair_dict["drug_names"].append(drug_name)
triple_pair_dict["drug_meshs"].append(drug_mesh)
triple_pair_dict["drug_drugbanks"].append(drug_drugbank)
triple_pair_dict["drug_target_names"].append(drug_target_name)
triple_pair_dict["drug_target_uniprots"].append(drug_target_uniprot)
triple_pair_dict["disease_names"].append(disease_name)
triple_pair_dict["disease_meshs"].append(disease_mesh)

elif node["id"] == target and node["label"] in ["Drug","ChemicalSubstance"]:
if entry["links"][i+1]["source"] == node["id"]:
new_target = entry["links"][i+1]["target"]
for node in nodes:
if (node["id"] == new_target) and (node["label"] == "Protein"):
drug_target_name = node["name"]
drug_target_uniprot = node["id"].replace('UniProt:', 'UniProtKB:')

triple_pair_dict["dmdb_ids"].append(dmdb_id)
triple_pair_dict["drug_names"].append(drug_name)
triple_pair_dict["drug_meshs"].append(drug_mesh)
triple_pair_dict["drug_drugbanks"].append(drug_drugbank)
triple_pair_dict["drug_target_names"].append(drug_target_name)
triple_pair_dict["drug_target_uniprots"].append(drug_target_uniprot)
triple_pair_dict["disease_names"].append(disease_name)
triple_pair_dict["disease_meshs"].append(disease_mesh)
else:
continue
# print(len(triple_pair_dict["dmdb_ids"]))
# print(len(triple_pair_dict["drug_meshs"]))
# print(len(triple_pair_dict["drug_drugbanks"]))
# print(len(triple_pair_dict["drug_target_names"]))
# print(len(triple_pair_dict["drug_target_uniprots"]))
# print(len(triple_pair_dict["disease_meshs"]))
df = pd.DataFrame(triple_pair_dict)
print(len(df))
csv_file_name = os.path.join(self.data_path,"indication_paths.csv")
df.to_csv(csv_file_name)

#TODO Figure out how to parse the triple store as a dictionary
extractor = Extractor(file_writer=self.output_file_writer)
with open(csv_file_name, 'rt') as fp:
extractor.csv_extract(fp,
lambda line: line[6], # subject id
lambda line: line[8], # object id
lambda line: "biolink:target_for",
lambda line: {}, #Node 1 props
lambda line: {}, #Node 2 props
lambda line: {}, #Edge props
comment_character=None,
delim=",",
has_header_row=True
)
return extractor.load_metadata

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