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

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    Type
    Article
    Authors
    Olayan, Rawan S. cc
    Ashoor, Haitham cc
    Bajic, Vladimir B. cc
    KAUST 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/1/1606-01-01
    URF/1/1976-02
    Date
    2017-11-24
    Online Publication Date
    2017-11-24
    Print Publication Date
    2018-04-01
    Permanent link to this record
    http://hdl.handle.net/10754/626259
    
    Metadata
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    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.
    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
    Bioinformatics
    DOI
    10.1093/bioinformatics/btx731
    10.1093/bioinformatics/bty417
    PubMed ID
    29186331
    Additional Links
    https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btx731/4657065
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    • 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
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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