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    Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches

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    RawanOlayan_PhD_Dissertation_Final.pdf
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    RawanOlayan_PhD_Dissertation_Final
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    Type
    Dissertation
    Authors
    Olayan, Rawan S. cc
    Advisors
    Bajic, Vladimir B. cc
    Committee members
    Moshkov, Mikhail cc
    Laleg-Kirati, Taous-Meriem cc
    Christoffels, Alan
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2017-12
    Embargo End Date
    2018-12-25
    Permanent link to this record
    http://hdl.handle.net/10754/626424
    
    Metadata
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    Access Restrictions
    At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2018-12-25.
    Abstract
    Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient strategy for systematic screening of a large number of drugs in the attempt to identify new DTIs at low cost and with reasonable accuracy. This necessitates development of accurate computational methods that can help focus on the follow-up experimental validation on a smaller number of highly likely targets for a drug. Although many methods have been proposed for computational DTI prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs. In this report, first, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithm-centric perspectives. The aim is to provide a comprehensive review of computational methods for identifying DTIs, which could help in constructing more reliable methods. Then, we present DDR, an efficient method to predict the existence of DTIs. DDR achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify 5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test setups, and using different performance measures, we show that DDR-FE achieves extremely good performance. Using blind test data, we verified as correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.
    Citation
    Olayan, R. S. (2017). Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. KAUST Research Repository. https://doi.org/10.25781/KAUST-30692
    DOI
    10.25781/KAUST-30692
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-30692
    Scopus Count
    Collections
    PhD Dissertations; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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