DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

Handle URI:
http://hdl.handle.net/10754/626259
Title:
DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Authors:
Olayan, Rawan S. ( 0000-0001-9148-2129 ) ; Ashoor, Haitham ( 0000-0003-2527-0317 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
Motivation Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using five repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new, and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Olayan RS, Ashoor H, Bajic VB (2017) DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx731.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
BAS/1/1606-01-01; URF/1/1976-02
Issue Date:
23-Nov-2017
DOI:
10.1093/bioinformatics/btx731
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Base Research Funds to VBB (BAS/1/1606-01-01) and by KAUST Office of Sponsored Research Grant No. URF/1/1976-02.
Additional Links:
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btx731/4657065
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorOlayan, Rawan S.en
dc.contributor.authorAshoor, Haithamen
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2017-11-30T13:07:31Z-
dc.date.available2017-11-30T13:07:31Z-
dc.date.issued2017-11-23en
dc.identifier.citationOlayan RS, Ashoor H, Bajic VB (2017) DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx731.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.doi10.1093/bioinformatics/btx731en
dc.identifier.urihttp://hdl.handle.net/10754/626259-
dc.description.abstractMotivation Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using five repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new, and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.en
dc.description.sponsorshipResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Base Research Funds to VBB (BAS/1/1606-01-01) and by KAUST Office of Sponsored Research Grant No. URF/1/1976-02.en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btx731/4657065en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.titleDDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approachesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorOlayan, Rawan S.en
kaust.authorAshoor, Haithamen
kaust.authorBajic, Vladimir B.en
kaust.grant.numberBAS/1/1606-01-01en
kaust.grant.numberURF/1/1976-02en
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