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Improving Strategy for Discovering Interacting Genetic Variants in Association Studies

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Book cover Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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Abstract

Revealing the underlying complex architecture of human diseases has received considerable attention since the exploration of genotype-phenotype relationships in genetic epidemiology. Identification of these relationships becomes more challenging due to multiple factors acting together or independently. A deep neural network was trained in the previous work to identify two-locus interacting single nucleotide polymorphisms (SNPs) related to a complex disease. The model was assessed for all two-locus combinations under various simulated scenarios. The results showed significant improvements in predicting SNP-SNP interactions over the existing conventional machine learning techniques. Furthermore, the findings are confirmed on a published dataset. However, the performance of the proposed method in the higher-order interactions was unknown. The objective of this study is to validate the model for the higher-order interactions in high-dimensional data. The proposed method is further extended for unsupervised learning. A number of experiments were performed on the simulated datasets under same scenarios as well as a real dataset to show the performance of the extended model. On an average, the results illustrate improved performance over the previous methods. The model is further evaluated on a sporadic breast cancer dataset to identify higher-order interactions between SNPs. The results rank top 20 higher-order SNP interactions responsible for sporadic breast cancer.

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Authors and Affiliations

  1. Department of Computing, Curtin University, Perth, Australia

    Suneetha Uppu & Aneesh Krishna

Authors
  1. Suneetha Uppu
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  2. Aneesh Krishna
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Corresponding author

Correspondence to Suneetha Uppu .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Uppu, S., Krishna, A. (2016). Improving Strategy for Discovering Interacting Genetic Variants in Association Studies. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_51

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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