Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach.

ABSTRACT:

Background

Early detection of sepsis is essential for its successful management. Although genome-wide association studies (GWAS) have shown potential in identifying sepsis-related genetic variants, they often involve heterogeneous patient groups and use single-locus analysis methods. Here, we aim to identify new sepsis susceptibility loci in post-surgical patients using an explainable artificial intelligence (XAI) approach applied to GWAS data.

Methods

GWAS was performed in 750 post-operative patients with sepsis and 3,500 population controls. We applied a novel XAI-based methodology to GWAS-derived single nucleotide polymorphisms (SNPs) to predict sepsis and prioritize new genetic variants associated with post-operative sepsis susceptibility. We also assessed functional and

SUBMITTER: Vaquerizo-Villar F 

PROVIDER: S-EPMC12745203 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature