Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies

Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biologi...

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Bibliographic Details
Main Authors: Stephane Wenric, Ruhollah Shemirani
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-08-01
Series:Frontiers in Genetics
Subjects:
RNA-Seq
supervised learning
random forests
variational autoencoders
gene selection
feature selection
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2018.00297/full

Internet

https://www.frontiersin.org/article/10.3389/fgene.2018.00297/full

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