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

The phenomenon of antimicrobial resistance (AMR) often results in treatment failure and restrictions on precision medicine, emphasizing the need for molecular diagnosis of drug resistance. The current use of machine learning (ML) techniques based on whole genome sequencing (WGS) data offers a more precise prediction of phenotypes. We incorporated WGS data from 3979 Staphylococcus aureus strains in our study. We modeled 10 common antibiotics using three types of features: gene, single nucleotide polymorphism (SNP), and k-mer to identify the best model and to determine which feature values most significantly contributed to the model's performance. The area under the curve (AUC) values of 40 mL models for 10 antibiotics ranged from 0.8345 to 0.9995. We noted that the performance indices such as the AUC of the gene model (0.9311-0.9992) and the integrated model (0.9313-0.9995) were markedly better than the SNP model (0.8345-0.9933) and the k-mer model (0.9024-0.9969). The best model AUC values for six antibiotics-cefoxitin, tetracycline, methicillin, gentamicin, erythromycin, and clindamycin-were over 0.99; nine antibiotic models had AUC values over 0.96, and all could effectively predict AMR phenotypes. Additionally, we discovered that certain non-AMR genes, such as the X998_03220 gene, significantly contributed to drug resistance prediction and overlapped in various antibiotic-related models simultaneously. Our study developed ML models that can reliably predict AMR phenotypes for commonly used antibiotics in S. aureus. We also identified potential molecular markers that can contribute to precision medicine implementation and healthcare cost reduction.

In our study, we developed a machine learning (ML) model that reliably predicts the antimicrobial resistance (AMR) phenotypes of Staphylococcus aureus 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 S. aureus. The expedited implementation of early and targeted antimicrobial therapies for S. aureus infections is essential for advancing precision medicine and can potentially lead to significant healthcare cost savings.