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Convolutional Model for Predicting SNP Interactions

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

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Abstract

Single-nucleotide polymorphisms (SNPs) are genetic markers that empower researchers to examine for genes associated with complex diseases. Several efforts have been contributed by researchers to study the interaction effects between multi-locus SNPs for discerning the status of complex diseases. However, the current conventional machine learning techniques are still left with several caveats. Deep learning is a new breed of machine learning technique that elucidates the hidden structure of the raw data by transforming it into multiple high levels of abstractions, using the power of parallel and distributed computing. It promises empirical success in the number of applications including bioinformatics to drive insights of biological complexities. The deep learning approach in the multi-locus interaction studies is yet to meet its potential achievements. In this paper, a convolutional neural network is trained to identify true causative two-locus SNP interactions. The performance of the method is evaluated on hypertension data. Highly ranked two-locus SNP interactions are identified for the manifestation of hypertension.

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Authors and Affiliations

  1. School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, Perth, Australia

    Suneetha Uppu & Aneesh Krishna

Authors
  1. Suneetha Uppu
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  2. Aneesh Krishna
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Correspondence to Suneetha Uppu .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Uppu, S., Krishna, A. (2018). Convolutional Model for Predicting SNP Interactions. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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