Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data.

ABSTRACT: With the advent of high-throughput technologies, life sciences are generating a huge amount of varied biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed in a cell or in a tissue under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed with relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly attributing to a particular phenotype or condition, (such as cancer), de novo. For identifying the key genes from gene expression data, among the existing literature, mutual information (MI) is one of the most successful criteria. However, the correction of MI for finite sample is not taken into account in this

SUBMITTER: Haque MN 

PROVIDER: S-EPMC8494339 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature