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    GCN-MF: Disease-gene association identification by graph convolutional networks and matrix factorization

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    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Han, Peng
    Shang, Shuo
    Yang, Peng
    Liu, Yong
    Zhao, Peilin
    Zhou, Jiayu
    Gao, Xin cc
    Kalnis, Panos cc
    KAUST Department
    King Abdullah University of Science and Technology
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-07-26
    Online Publication Date
    2019-07-26
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/656764
    
    Metadata
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    Abstract
    Discovering disease-gene association is a fundamental and critical biomedical task, which assists biologists and physicians to discover pathogenic mechanism of syndromes. With various clinical biomarkers measuring the similarities among genes and disease phenotypes, network-based semi-supervised learning (NSSL) has been commonly utilized by these studies to address this class-imbalanced large-scale data issue. However, most existing NSSL approaches are based on linear models and suffer from two major limitations: 1) They implicitly consider a local-structure representation for each candidate; 2) They are unable to capture nonlinear associations between diseases and genes. In this paper, we propose a new framework for disease-gene association task by combining Graph Convolutional Network (GCN) and matrix factorization, named GCN-MF. With the help of GCN, we could capture nonlinear interactions and exploit measured similarities. Moreover, we define a margin control loss function to reduce the effect of sparsity. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.
    Citation
    Han, P., Yang, P., Zhao, P., Shang, S., Liu, Y., Zhou, J., … Kalnis, P. (2019). GCN-MF. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3292500.3330912
    Publisher
    Association for Computing Machinery (ACM)
    Conference/Event name
    25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
    DOI
    10.1145/3292500.3330912
    Additional Links
    http://dl.acm.org/citation.cfm?doid=3292500.3330912
    ae974a485f413a2113503eed53cd6c53
    10.1145/3292500.3330912
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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