DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Type
ArticleKAUST Department
Computational Bioscience Research Center (CBRC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Applied Mathematics and Computational Science Program
KAUST Grant Number
BAS/1/1606-01-01URF/1/1976-02
Date
2017-11-24Online Publication Date
2017-11-24Print Publication Date
2018-04-01Permanent link to this record
http://hdl.handle.net/10754/626259
Metadata
Show full item recordAbstract
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.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.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.Publisher
Oxford University Press (OUP)Journal
BioinformaticsPubMed ID
29186331Additional Links
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btx731/4657065Relations
Is Supplemented By:- Data and code
Code for "DDR: a method to predict drug target interactions using multiple similarities". URL: https://bitbucket.org/RSO24/ddr/ HANDLE: 10754/656600
ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btx731
Scopus Count
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