Machine learning based refined differential gene expression analysis of pediatric sepsis

• Published in: BMC medical genomics Vol. 13; no. 1; pp. 122 - 10
• Main Authors: Abbas, Mostafa, EL-Manzalawy, Yasser
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.08.2020; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Biomarkers; Biomarkers - analysis; Biomarkers discovery; Child; Computational Biology - methods; Critical care; Differential expression analysis; Experiments; Feature selection; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Genes; Genomes; Genomics; Health aspects; Humans; Learning algorithms; Machine Learning; Methods; Mortality; Pediatrics; Refined differential gene expression analysis; ROC Curve; Sepsis; Sepsis - genetics; Sepsis - pathology; Septic shock; Technical Advance; Transcriptome; Feature selection; Biomarkers discovery; Refined differential gene expression analysis; Differential expression analysis
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-020-00771-4

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. In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89. Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.