Genomic and machine learning approaches to predict antimicrobial resistance in <i>Stenotrophomonas maltophilia</i>.

ABSTRACT: Stenotrophomonas maltophilia is a multidrug-resistant pathogen, which poses a major challenge to clinical management due to its increasing resistance to common antibiotics, such as levofloxacin (LEV) and trimethoprim-sulfamethoxazole (SXT), and poor clinical response to treatment. There is an urgent need for rapid and reliable antimicrobial susceptibility testing (AST) methods to improve treatment outcomes. This study collected 441 S. maltophilia strains, performed whole-genome sequencing, and used machine learning to identify key resistance determinants for LEV and SXT, constructing predictive models for resistance phenotypes. The 441 S. maltophilia strains we collected show significant genomic diversity and representative lineage distribution. Machine learning

SUBMITTER: Liu X 

PROVIDER: S-EPMC12323353 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature