GSEA-SDBE: A gene selection method for breast cancer classification based on GSEA and analyzing differences in performance metrics.

ABSTRACT:

Motivation

Selecting the most relevant genes for sample classification is a common process in gene expression studies. Moreover, determining the smallest set of relevant genes that can achieve the required classification performance is particularly important in diagnosing cancer and improving treatment.

Results

In this study, I propose a novel method to eliminate irrelevant and redundant genes, and thus determine the smallest set of relevant genes for breast cancer diagnosis. The method is based on random forest models, gene set enrichment analysis (GSEA), and my developed Sort Difference Backward Elimination (SDBE) algorithm; hence, the method is named GSEA-SDBE. Using this method, genes are filtered according to their importance following random forest training and GSEA is

SUBMITTER: Ai H 

PROVIDER: S-EPMC9041804 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature