1- Drugbank
RDF Dataset Obtained from: http://download.bio2rdf.org/release/4/drugbank/drugbank.nq.gz
Original complete Dataset Obtained from : https://www.drugbank.ca/releases/latest
2- Kegg
RDF Dataset Obtained from: http://download.bio2rdf.org/release/4/kegg/kegg-drug.nq.gz http://download.bio2rdf.org/release/4/kegg/kegg-genes.nq.gz
Original complete Dataset Obtained from : ftp://ftp.genome.jp/pub/kegg/
3- SIDER
RDF Dataset Obtained from: http://download.bio2rdf.org/release/4/sider/sider-se.nq.gz
Original complete Dataset Obtained from: http://sideeffects.embl.de/media/download/
4- NDF-RT
Original complete Dataset Obtained from: http://evs.nci.nih.gov/ftp1/NDF-RT/NDF-RT_XML.zip
5- MEDDRA
RDF Dataset Obtained from: http://purl.bioontology.org/ontology/MEDDRA
6- Drug Indication from NDR-FT and DrugCentral
RDF Data generated: data/input/unified-gold-standard-umls.nq.gz
PREFIX iv: <http://bio2rdf.org/irefindex_vocabulary:>
PREFIX dv: <http://bio2rdf.org/drugbank_vocabulary:>
PREFIX hv: <http://bio2rdf.org/hgnc_vocabulary:>
PREFIX kv: <http://bio2rdf.org/kegg_vocabulary:>
SELECT distinct ?drugid ?geneid
WHERE
{
?drug <http://bio2rdf.org/openpredict_vocabulary:indication> ?i .
{
?d a kv:Drug .
?d kv:target ?l .
?l kv:link ?t .
BIND (URI( REPLACE(str(?t),"HSA","hsa")) AS ?target) .
?target a kv:Gene .
?target kv:x-ncbigene ?ncbi .
#?target kv:x-uniprot ?ncbi .
?d kv:x-drugbank ?drug .
}
UNION
{
?drug a dv:Drug .
?drug dv:target ?target .
?target dv:x-hgnc ?hgnc .
?hgnc hv:x-ncbigene ?ncbi .
#?hgnc hv:uniprot ?ncbi .
}
BIND ( STRAFTER(str(?ncbi),"http://bio2rdf.org/ncbigene:") AS ?geneid)
BIND( STRAFTER(str(?drug), "http://bio2rdf.org/drugbank:") AS ?drugid)
}PREFIX dv: <http://bio2rdf.org/drugbank_vocabulary:>
SELECT distinct ?drugid ?smiles
{
?d <http://bio2rdf.org/openpredict_vocabulary:indication> ?i .
?d dv:calculated-properties ?cp .
?cp a dv:SMILES .
?cp dv:value ?smiles .
BIND( STRAFTER(str(?d), "http://bio2rdf.org/drugbank:") AS ?drugid)
}PREFIX dv: <http://bio2rdf.org/drugbank_vocabulary:>
PREFIX sider: <http://bio2rdf.org/sider_vocabulary:>
SELECT distinct ?drugid ?se
{
?dia <http://bio2rdf.org/sider_vocabulary:drug> ?stitch_flat .
?dia <http://bio2rdf.org/sider_vocabulary:effect> ?se .
{
?stitch_flat <http://bio2rdf.org/sider_vocabulary:x-pubchem.compound> ?pc .
}
UNION{
?stitch_flat <http://bio2rdf.org/sider_vocabulary:stitch-stereo> ?stitch_stereo .
?stitch_stereo <http://bio2rdf.org/sider_vocabulary:x-pubchem.compound> ?pc .
}
?d dv:x-pubchemcompound ?pc .
BIND(STRAFTER( str(?d), "http://bio2rdf.org/drugbank:") AS ?drugid)
}PREFIX sider: <http://bio2rdf.org/sider_vocabulary:>
SELECT distinct ?umlsid ?parent_id
WHERE {
?drug <http://bio2rdf.org/openpredict_vocabulary:indication> ?i .
?a <http://bioportal.bioontology.org/ontologies/umls/cui> ?i .
?a rdfs:subClassOf* ?parent .
FILTER (regex(str(?parent),"^http://bio2rdf.org/ndfrt:")
|| regex(str(?parent),"^http://bio2rdf.org/meddra:") )
BIND (STRAFTER(str(?i),"http://bio2rdf.org/umls:") AS ?umlsid)
BIND (STRAFTER(str(?parent),"http://bio2rdf.org/") AS ?parent_id)
}
export SPARQL_ENDPOINT=http://localhost:13065/sparql
#query drug target info and downlad in the input folder
curl -H "Accept: text/tab-separated-values" --data-urlencode query@drugbank-drug-target.sparql $SPARQL_ENDPOINT > drugbank-drug-target.tab
#query drug smiles info and downlad in the input folder
curl -H "Accept: text/tab-separated-values" --data-urlencode query@drugbank-drug-smiles.sparql $SPARQL_ENDPOINT > drugbank-drug-smiles.tab
curl -H "Accept: text/tab-separated-values" --data-urlencode query@sider-meddra-terms.sparql $SPARQL_ENDPOINT > sider-meddra-terms.tab
python 3.6
scikit-learn
pandas
numpy python createTargetFeatureMatrix.py ../data/input/drugbank-drug-target.tab > ../data/features/drugs-targets.txt
#create feature matrix for drug fingerprint
python createFingerprintFeatures.py ../data/input/drugbank-drug-smiles.tab > ../data/features/drugs-fingerprint.txt
#disease meddra features
python createMeddraFeatures.py ../data/input/sider-meddra-terms.tab > ../data/features/diseases-meddra.txtdo 10-fold cross-validation, separate gold standard into train and test by removing 10% of drugs and theirs association train on training set and test on test set and report the performance of each fold
python cv_test.py -g ../data/input/unified-gold-standard-umls.txt -dr ../data/features/drugs-targets.txt ../data/features/drugs-fingerprint.txt ../data/features/drugs-sider-se.txt -di ../data/features/diseases-ndfrt-meddra.txt -o ../data/output/completeset_unified_validation.txt -disjoint 0 -p 2 -m rfCommand Line
usage: cv_test.py -g GOLDINDICATIONS -m MODELTYPE -disjoint DISJOINT -o
OUTPUT -p PROPORTION -dr DRUGFEAT [DRUGFEAT ...] -di
DISEASEFEAT [DISEASEFEAT ...]
-g enter path to the file for drug indication gold standard
-dr paths to the files for drug features
-di enter paths to the files for disease features
-m classification model name ( logistic | knn | tree | rf | gbc )
-disjoint enter disjoint [0,1,2] (0: Pair-wise, 1 : Drug-wise 2: Disease-wise Disjoint)
-o path to output file for model
-p number of proportion for negative samples
train with whole gold standard set
python train_and_test.py -g ../data/input/unified-gold-standard-umls.txt -t ../data/predictions/PREDICT-repositioned-drug-mapped.csv -dr ../data/features/drugs-targets.txt ../data/features/drugs-fingerprint.txt ../data/features/drugs-sider-se.txt -di ../data/features/diseases-ndfrt-meddra.txt -s 405 -o ../data/predictions/rf_p2_n405.txt -m rf -p 2
usage: train_and_test.py -g GOLDINDICATIONS -t TEST -m MODELTYPE -o OUTPUT -p
PROPORTION -s SEED -dr DRUGFEAT [DRUGFEAT ...] -di
DISEASEFEAT [DISEASEFEAT ...]
-g enter path to the file for drug indication gold standard
-t enter path to the file you want to get predictions for
-dr paths to the files for drug features
-s enter seed number
-di enter paths to the files for disease features
-m classification model name ( logistic | knn | tree | rf | gbc )
-o path to output file for model
-p number of proportion for negative samples
If you use the code of this repository for your reserch, please consider citing the following paper:
@inproceedings{celebi2017machine,
title={Machine Learning Based Drug Indication Prediction Using Linked Open Data.},
author={Celebi, Remzi and Erten, {\"O}zg{\"u}n and Dumontier, Michel},
booktitle={Semantic Web Applications and Tools for Healthcare and Life Sciences (SWAT4HCLS)},
year={2017}
}