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

ABSTRACT: 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 biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships. Differential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive. Here, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample

SUBMITTER: Wenric S 

PROVIDER: S-EPMC6085558 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature