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dc.contributor.authorUppu, Suneetha
dc.contributor.supervisorAneesh Krishnaen_US

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.

dc.publisherCurtin Universityen_US
dc.titleDiscovering Higher-order SNP Interactions in High-dimensional Genomic Dataen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US

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