Identification of m5C-Related gene diagnostic biomarkers for sepsis: a machine learning study.

ABSTRACT:

Background

Sepsis is a serious condition that occurs when the body's response to infection becomes uncontrolled, resulting in a high risk of death. Despite improvements in healthcare, identifying sepsis early is difficult because of its diverse nature and the absence of distinct biomarkers. Recent studies suggest that 5-methylcytosine (m5C)-related genes play a significant role in immune responses, yet their diagnostic potential in sepsis remains unexplored.

Methods

This research combined and examined four sepsis-related datasets (GSE95233, GSE57065, GSE100159, and GSE65682) sourced from the Gene Expression Omnibus (GEO)database to discover m5C-related genes with differential expression. Various machine learning methods, such as decision tree, random forest, and XGBoost, wer

SUBMITTER: Lin S 

PROVIDER: S-EPMC11558340 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature