Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance - Yu, Yanying; Wheeler, Nicole E.; Barquist, Lars (PLOS Biology, 2025)
Machine learning methods have emerged as promising tools to predict antimicrobial resistance (AMR) and uncover resistance determinants from genomic data. This study shows that sampling biases driven by population structure severely undermine the accuracy of AMR prediction models even with large datasets, providing recommendations for evaluating the accuracy of future methods.