A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Staphylococcus aureus Through Machine Learning Analysis of Genome Data - Wang, Shuyi; Zhao, Chunjiang; Yin, Yuyao; Chen, Fengning; Chen, Hongbin; Wang, Hui (Frontiers in Microbiology, 2022)
With the reduction in sequencing price and acceleration of sequencing speed, it is particularly important to directly link the genotype and phenotype of bacteria. Here, we firstly predicted the minimum inhibitory concentrations of ten antimicrobial agents for Staphylococcus aureus using 466 isolates by directly extracting k-mer from whole genome sequencing combined with three machine learning algorithms: random forest, support vector machine, and XGBoost. Considering one two-fold dilution, the essential agreement and the category agreement could reach >85% and >90% for most antimicrobial agents. For clindamycin, cefoxitin and trimethoprim-sulfamethoxazole, the essential agreement and the category agreement could reach >91% and >93%, providing important information for clinical treatment. The successful prediction of cefoxitin resistance showed that the model could distinguish methicillin-resistant Staphylococcus aureus. The results suggest that small datasets available in large hospitals could bypass the existing basic research and known antimicrobial resistance genes, accurately predict the bacterial phenotype.