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Epistasis Analysis: Classification Through Machine Learning Methods

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Epistasis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2212))

Abstract

Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Linjing Liu

  2. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

Authors
  1. Linjing Liu
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  2. Ka-Chun Wong
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Corresponding author

Correspondence to Ka-Chun Wong .

Editor information

Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Liu, L., Wong, KC. (2021). Epistasis Analysis: Classification Through Machine Learning Methods. In: Wong, KC. (eds) Epistasis. Methods in Molecular Biology, vol 2212. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0947-7_21

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  • DOI: https://doi.org/10.1007/978-1-0716-0947-7_21

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0946-0

  • Online ISBN: 978-1-0716-0947-7

  • eBook Packages: Springer Protocols

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