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Brief Survey on Machine Learning in Epistasis

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Epistasis

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

Abstract

In biology, the term “epistasis” indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interaction can have a strong effect on the function of the two genes. These changes then can alter the consequences of the biological processes, influencing the organism’s phenotype. Machine learning is an area of computer science that develops statistical methods able to recognize patterns from data. A typical machine learning algorithm consists of a training phase, where the model learns to recognize specific trends in the data, and a test phase, where the trained model applies its learned intelligence to recognize trends in external data. Scientists have applied machine learning to epistasis problems multiple times, especially to identify gene–gene interactions from genome-wide association study (GWAS) data. In this brief survey, we report and describe the main scientific articles published in data mining and epistasis. Our article confirms the effectiveness of machine learning in this genetics subfield.

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Acknowledgements

The authors thank Ka Chun Wong (City University of Hong Kong) for having curated this book.

Author information

Authors and Affiliations

  1. Krembil Research Institute, Toronto, Ontario, Canada

    Davide Chicco

  2. Toronto General Hospital, Toronto, Ontario, Canada

    Trent Faultless

Authors
  1. Davide Chicco
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  2. Trent Faultless
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Corresponding author

Correspondence to Davide Chicco .

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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Chicco, D., Faultless, T. (2021). Brief Survey on Machine Learning in Epistasis. 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_11

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  • DOI: https://doi.org/10.1007/978-1-0716-0947-7_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0946-0

  • Online ISBN: 978-1-0716-0947-7

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