A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis.

ABSTRACT: Sepsis is a complex systemic inflammatory syndrome that currently lacks stable and specific biomarkers. Multi-omics integration combined with machine learning and single-cell analysis offers new approaches for elucidating molecular mechanisms and nominating candidate regulators for further validation. Establish an integrated analytical framework combining transcriptomics, proteomics, metabolomics, and single-cell transcriptomics to identify sepsis-associated candidates and generate testable hypotheses for downstream mechanistic and translational studies. A multi-omics integrative framework was established by combining transcriptomic, proteomic, and metabolomic data. Weighted gene co-expression network analysis (WGCNA) was first applied to identify modules significantly associated with seps

SUBMITTER: Li X 

PROVIDER: S-EPMC12914058 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature