Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity.

ABSTRACT: COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determine

SUBMITTER: Lipman D 

PROVIDER: S-EPMC9038205 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature