DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/626259
MetadataShow full item record
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.
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.
SponsorsResearch 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.
PublisherOxford University Press (OUP)
- DASPfind: new efficient method to predict drug-target interactions.
- Authors: Ba-Alawi W, Soufan O, Essack M, Kalnis P, Bajic VB
- Issue date: 2016
- Predicting drug-target interactions using restricted Boltzmann machines.
- Authors: Wang Y, Zeng J
- Issue date: 2013 Jul 1
- A Drug-Side Effect Context-Sensitive Network approach for Drug Target Prediction.
- Authors: Zhou M, Chen Y, Xu R
- Issue date: 2018 Nov 14
- Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.
- Authors: Zong N, Kim H, Ngo V, Harismendy O
- Issue date: 2017 Aug 1
- DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.
- Authors: Yuan Q, Gao J, Wu D, Zhang S, Mamitsuka H, Zhu S
- Issue date: 2016 Jun 15