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    Computational Drug-target Interaction Prediction based on Graph Embedding and Graph Mining

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    computational.pdf
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    Type
    Conference Paper
    Authors
    Thafar, Maha A.
    Albaradie, Somayah
    Olayan, Rawan S. cc
    Ashoor, Haitham cc
    Essack, Magbubah cc
    Bajic, Vladimir B. cc
    KAUST Department
    King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia Taif University, College of Computers and Information Technology, Taif, Saudi Arabia
    King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-05-18
    Online Publication Date
    2020-05-18
    Print Publication Date
    2020-01-19
    Permanent link to this record
    http://hdl.handle.net/10754/663808
    
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    Abstract
    Identification of interactions of drugs and proteins is an essential step in the early stages of drug discovery and in finding new drug uses. Traditional experimental identification and validation of these interactions are still time-consuming, expensive, and do not have a high success rate. To improve this identification process, development of computational methods to predict and rank likely drug-target interactions (DTI) with minimum error rate would be of great help. In this work, we propose a computational method for (Drug-Target interaction prediction using Graph Embedding and graph Mining), DTiGEM. DTiGEM models identify novel DTIs as a link prediction problem in a heterogeneous graph constructed by integrating three networks, namely: drug-drug similarity, target-target similarity, and known DTIs. DTiGEM combines different techniques, including graph embeddings (e.g., node2vec), graph mining (e.g., path scores between drugs and targets), and machine learning (e.g., different classifiers). DTiGEM achieves improvement in the prediction performance compared to other state-of-the-art methods for computational prediction of DTIs on four benchmark datasets in terms of area under precision-recall curve (AUPR). Specifically, we demonstrate that based on the average AUPR score across all benchmark datasets, DTiGEM achieves the highest average AUPR value (0.831), thus reducing the prediction error by 22.4% relative to the second-best performing method in the comparison.
    Citation
    Thafar, M. A., Albaradie, S., Olayan, R. S., Ashoor, H., Essack, M., & Bajic, V. B. (2020). Computational Drug-target Interaction Prediction based on Graph Embedding and Graph Mining. Proceedings of the 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics. doi:10.1145/3386052.3386062
    Publisher
    Association for Computing Machinery (ACM)
    ISBN
    9781450376761
    DOI
    10.1145/3386052.3386062
    Additional Links
    https://dl.acm.org/doi/10.1145/3386052.3386062
    ae974a485f413a2113503eed53cd6c53
    10.1145/3386052.3386062
    Scopus Count
    Collections
    Conference Papers; 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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