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    Novel Methods for Drug-Target Interaction Prediction using Graph Mining

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    PhD Dissertation - Wail Ba alawi - 101741[1] copy.pdf
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    3.308Mb
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    Type
    Dissertation
    Authors
    Ba Alawi, Wail cc
    Advisors
    Bajic, Vladimir B. cc
    Committee members
    Kalnis, Panos cc
    Arold, Stefan T. cc
    Hide, Winston
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-08-31
    Embargo End Date
    2017-09-01
    Permanent link to this record
    http://hdl.handle.net/10754/619165
    
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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 2017-09-01.
    Abstract
    The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction accuracy and proposed an enhanced strategy for DTI prediction. Our results show significant improvements of the accuracy of the top ranked DTI prediction over the current state-of-the-art methods.
    Citation
    Ba Alawi, W. (2016). Novel Methods for Drug-Target Interaction Prediction using Graph Mining. KAUST Research Repository. https://doi.org/10.25781/KAUST-560OM
    DOI
    10.25781/KAUST-560OM
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-560OM
    Scopus Count
    Collections
    Dissertations; Dissertations; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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