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    A multifactor dimensionality reduction based associative classification for detecting SNP interactions

    Access Status
    Fulltext not available
    Authors
    Uppu, S.
    Krishna, Aneesh
    Gopalan, R.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Uppu, S. and Krishna, A. and Gopalan, R. 2015. A multifactor dimensionality reduction based associative classification for detecting SNP interactions, in Arik S. and Huang T. and Lai W. and Liu Q. (ed), Proceedings of the 22nd International Conference ICONIP 2015, Nov 9-12 2015, pp. 328-336. Istanbul: Springer.
    Source Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    DOI
    10.1007/978-3-319-26532-2_36
    ISBN
    9783319265315
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/32727
    Collection
    • Curtin Research Publications
    Abstract

    Identification and characterization of interactions between genes have been increasingly explored in current Genome-wide association studies (GWAS). Several machine learning and data mining approaches have been proposed to identify the multi-locus interactions in higher order genomic data. However, detecting these interactions is challenging due to bio-molecular complexities and computational limitations. In this paper, a multifactor dimensionality reduction based associative classifier is proposed for detecting SNP interactions in genetic epidemiological studies. The approach is evaluated for one to six loci models by varying heritability, minor allele frequency, case-control ratios and sample size. The experimental results demonstrated significant improvements in accuracy for detecting interacting single nucleotide polymorphisms (SNPs) responsible for complex diseases when compared to the previous approaches. Further, the approach was successfully evaluated by using sporadic breast cancer data. The results show interactions among five polymorphisms in three different estrogen-metabolism genes.

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