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    Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

    Uppu S 2018.pdf (12.79Mb)
    Access Status
    Open access
    Authors
    Uppu, Suneetha
    Date
    2018
    Supervisor
    Aneesh Krishna
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/77025
    Collection
    • Curtin Theses
    Abstract

    In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise.

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