Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for Staphylococcus aureus via metagenomic sequencing

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for Staphylococcus aureus via metagenomic sequencing

ABSTRACT 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 s...

Full description

Saved in:
Bibliographic Details
Main Authors: Xuefeng Jia, Yongfen Xiong, Yanping Xu, Fangyuan Chen, Peng Han, Jieming Qu, Quanli He, Guanhua Rao
Format: Article
Language:English
Published: American Society for Microbiology 2025-08-01
Series:Microbiology Spectrum
Subjects:
metagenomic sequencing
Staphylococcus aureus
machine learning
rule-based model
antibiotic susceptibility predictions
vancomycin
Online Access:https://journals.asm.org/doi/10.1128/spectrum.00556-25
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://journals.asm.org/doi/10.1128/spectrum.00556-25

Similar Items