Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

ABSTRACT: We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solut

SUBMITTER: Schaack D 

PROVIDER: S-EPMC8128240 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature