Machine learning based refined differential gene expression analysis of pediatric sepsis.

ABSTRACT:

Background

Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches.

Methods

In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ran

SUBMITTER: Abbas M 

PROVIDER: S-EPMC7453705 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature