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    A Deep Learning Approach to Detect SNP Interactions

    Access Status
    Fulltext not available
    Authors
    Uppu, S.
    Krishna, Aneesh
    Gopalan, R.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Uppu, S. and Krishna, A. and Gopalan, R. 2016. A Deep Learning Approach to Detect SNP Interactions. Journal of Software. 11 (10): pp. 965-975.
    Source Title
    Journal of Software
    DOI
    10.17706/jsw.11.10.965-975
    ISSN
    1796-217X
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/54475
    Collection
    • Curtin Research Publications
    Abstract

    The susceptibility of complex diseases are characterised by numerous genetic, lifestyle, and environmental causes individually or due to their interaction effects. The recent explosion in detecting genetic interacting factors is increasingly revealing the underlying biological networks behind complex diseases. Several computational methods are explored to discover interacting polymorphisms among unlinked loci. However, there has been no significant breakthrough towards solving this problem because of bio- molecular complexities and computational limitations. Our previous research trained a deep multilayered feedforward neural network to predict two-locus polymorphisms due to interactions in genome-wide data. The performance of the method was studied on numerous simulated datasets and a published genome-wide dataset. In this manuscript, the performance of the trained multilayer neural network is validated by varying the parameters of the models under various scenarios. Furthermore, the observations of the previous method are confirmed in this study by evaluating on a real dataset. The experimental findings on a real dataset show significant rise in the prediction accuracy over other conventional techniques. The result shows highly ranked interacting two-locus polymorphisms, which may be associated with susceptibility for the development of breast cancer.

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