Machine Learning-Selected Minimal Features Drive High-Accuracy Rule-Based Antibiotic Susceptibility Predictions for Staphylococcus aureus via metagenomic sequencing
Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) to guide timely treatments. Traditional culture-based AST methods are slow, while existing whole-genome sequencing (WGS)-based models often suffer from overfitting, poor interpretability, and diminished performance on clinical metagenomic data. In this study, we developed an interpretable genotypic AST approach for Staphylococcus aureus using minimal genomic determinants. Analysis of 4,796 S. aureus genomes and AST data for 18 antibiotics revealed 1 to 5 key resistance genes per antibiotic, including two previously uncharacterized vancomycin resistance markers.