A robust and stable gene selection algorithm based on graph theory and machine learning.

ABSTRACT:

Background

Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffer from being a very underdetermined system. In a very large set of features but a very small sample size domain (e.g. DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several contrasting feature subsets may yield near equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a robust and stable supervised gene selection algorithm to select a set of robust and stable genes having a better prediction

SUBMITTER: Saha S 

PROVIDER: S-EPMC8579680 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature