Bias in data may be blocking AI’s potential to combat antibiotic resistance

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.

In your coverage, please use this URL to provide access to the freely available paper in PLOS Biology: https://plos.io/44mryGI  

Article title: Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance

Author countries: United States, United Kingdom, Germany, Canada

Funding: This work was funded in part by the Bavarian State Ministry for Science and the Arts through the research network Bayresq.net (to L.B.), and an Natural Sciences and Engineering Research Council (NSERC, https://www.nserc-crsng.gc.ca/index_eng.asp) Discovery Grant (RGPIN-2024-04305 to L.B.). The funders played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.