Collective feature selection to identify crucial epistatic variants

Collective feature selection to identify crucial epistatic variants

Abstract Background Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small...

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Bibliographic Details
Main Authors: Shefali S. Verma, Anastasia Lucas, Xinyuan Zhang, Yogasudha Veturi, Scott Dudek, Binglan Li, Ruowang Li, Ryan Urbanowicz, Jason H. Moore, Dokyoon Kim, Marylyn D. Ritchie
Format: Article
Language:English
Published: BMC 2018-04-01
Series:BioData Mining
Subjects:
Feature selection
Epistasis
Non-additive effects
Obesity
Parametric methods
Non-parametric methods
Online Access:http://link.springer.com/article/10.1186/s13040-018-0168-6

Internet

http://link.springer.com/article/10.1186/s13040-018-0168-6

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