Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data.

ABSTRACT: Selection of biologically relevant genes from high-dimensional expression data is a key research problem in gene expression genomics. Most of the available gene selection methods are either based on relevancy or redundancy measure, which are usually adjudged through post selection classification accuracy. Through these methods the ranking of genes was conducted on a single high-dimensional expression data, which led to the selection of spuriously associated and redundant genes. Hence, we developed a statistical approach through combining a support vector machine with Maximum Relevance and Minimum Redundancy under a sound statistical setup for the selection of biologically relevant genes. Here, the genes were selected through statistical significance values and computed using a nonparametri

SUBMITTER: Das S 

PROVIDER: S-EPMC7712650 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature