Prediction of antimicrobial resistance in <i>Staphylococcus aureus</i> with a machine learning classifier based on WGS data

In our study, we developed a machine learning (ML) model that reliably predicts the antimicrobial resistance (AMR) phenotypes of <i>Staphylococcus aureus</i> to commonly used antibiotics. This model not only predicts AMR phenotypes but also identifies potential molecular markers, which could facilitate the implementation of precision medicine and contribute to reducing healthcare costs. The integration of diverse biomarker types is crucial for enhancing model performance; however, their effectiveness may vary depending on the specific antibiotic in question. Furthermore, our pan-genome-based characterization has revealed novel potential molecular markers associated with AMR, thereby enhancing our comprehension of the underlying molecular mechanisms of AMR in <i>S. aureus</i>. The expedited implementation of early and targeted antimicrobial therapies for <i>S. aureus</i> infections is essential for advancing precision medicine and can potentially lead to significant healthcare cost savings.