Collective feature selection to identify crucial epistatic variants

Abstract excerpt

<h4>Background</h4> 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 sample size as input, thus leading to the so-called “short fat data” problem. The efficiency of machine learning me...

Topics

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• Genomics
• Algorithms
• Epistasis, Genetic
• Gene Expression Regulation
• Genetic Association Studies
• Genetic Predisposition to Disease
• Machine Learning
• Models, Statistical

Identifiers and source

Literature Corpus work

0c31f698-a8d6-5132-9e0f-cb3dd89badbf

DOI

10.1101/293365