Integrative multi-omics and machine learning identify mitochondrial biomarkers for pathogen-specific sepsis stratification and translational prioritization

Key Points

• The aim is to link mitochondrial dysregulation to sepsis diversity and identify biomarkers for further study.
• Utilized a multi-omics framework to analyze mitochondrial function and its links to sepsis.
• Implemented machine learning techniques to evaluate model performance.
• Conducted internal resampling for model validation.
• Identified candidate biomarkers related to mitochondrial metrics for sepsis.
• Model performance indicates promise but requires validation in independent cohorts.