Epistasis Analysis: Classification Through Machine Learning Methods

Linjing Liu; Ka-Chun Wong

doi:10.1007/978-1-0716-0947-7_21

Epistasis Analysis: Classification Through Machine Learning Methods

Linjing Liu
, Ka-Chun Wong^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Epistasis
Subtitle of host publication	Methods and Protocols
Editors	Ka-Chun Wong
Place of Publication	New York, NY
Publisher	Humana Press
Pages	337-345
ISBN (Electronic)	9781071609477
ISBN (Print)	9781071609460, 9781071609491
DOIs	https://doi.org/10.1007/978-1-0716-0947-7_21
Publication status	Published - 2021

Publication series

Name	Methods in Molecular Biology
Volume	2212
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Research Keywords

Classification
Epistasis
Feature selection
Machine learning
Model evaluation

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10.1007/978-1-0716-0947-7_21

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Epistasis Analysis: Classification Through Machine Learning Methods. / Liu, Linjing ; Wong, Ka-Chun.
Epistasis: Methods and Protocols. ed. / Ka-Chun Wong. New York, NY: Humana Press, 2021. p. 337-345 (Methods in Molecular Biology; Vol. 2212).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

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AB - 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.

KW - Classification

KW - Epistasis

KW - Feature selection

KW - Machine learning

KW - Model evaluation

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Epistasis Analysis: Classification Through Machine Learning Methods

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