Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome

    226575_226575.pdf (728.5Kb)
    Access Status
    Open access
    Authors
    Uppu, S.
    Krishna, Aneesh
    Gopalan, Raj
    Date
    2014
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Uppu, S. and Krishna, A. and Gopalan, R. 2014. An associative classification based approach for detecting SNP-SNP interactions in high dimensional genome, in 14th Ieee International Conference on Bioinformatics and Bioengineering, Nov 10-12 2014. Boca Raton, Florida, USA: IEEE.
    Source Title
    14th Ieee International Conference on Bioinformatics and Bioengineering proceedings
    Source Conference
    14th Ieee International Conference on Bioinformatics and Bioengineering
    DOI
    10.1109/BIBE.2014.29
    ISBN
    978-1-4799-7501-3
    School
    Department of Computing
    Remarks

    Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/39547
    Collection
    • Curtin Research Publications
    Abstract

    There have been many studies that depict genotype phenotype relationships by identifying genetic variants associated with a specific disease. Researchers focus more attention on interactions between SNPs that are strongly associated with disease in the absence of main effect. In this context, a number of machine learning and data mining tools are applied to identify the combinations of multi-locus SNPs in higher order data.However, none of the current models can identify useful SNPSNP interactions for high dimensional genome data. Detecting these interactions is challenging due to bio-molecular complexities and computational limitations. The goal of this research was to implement associative classification and study its effectiveness for detecting the epistasis in balanced and imbalanced datasets. The proposed approach was evaluated for two locus epistasis interactions using simulated data. The datasets were generated for 5 different penetrance functions by varying heritability, minor allele frequency and sample size. In total, 23,400 datasets were generated and several experiments are conducted to identify the disease causal SNP interactions. The accuracy of classification by the proposed approach wascompared with the previous approaches. Though associative classification showed only relatively small improvement in accuracy for balanced datasets, it outperformed existing approaches in higher order multi-locus interactions in imbalanced datasets.

    Related items

    Showing items related by title, author, creator and subject.

    • Detecting SNP Interactions in Balanced and Imbalanced Datasets using Associative Classification
      Uppu, S.; Krishna, Aneesh; Gopalan, Raj (2014)
      The genetic epidemiology behind the complex diseases are characterised by multiple factors acting together or independently. The complex network of these multiple factors induces pathological mechanisms which lead to ...
    • Rule-based analysis for detecting epistasis using associative classification mining
      Uppu, Suneetha; Krishna, Aneesh; Gopalan, Raj (2015)
      The advancements in sequencing high-throughput human genome and computational abilities have tremendously improved the understanding of the genetic architecture behind the complex diseases. The development of high-throughput ...
    • Evaluation of associative classification-based multifactor dimensionality reduction in the presence of noise
      Krishna, Aneesh (2016)
      The advancements in genetic epidemiology have focused more on understanding the associations and functional relationships among the genes. Identifying the susceptible genes and their interaction effects over the complex ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.