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News Release 16-Dec-2025
Bias in data may be blocking AI’s potential to combat antibiotic resistance
Peer-Reviewed Publication
PLOS
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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.
Journal
PLOS Biology
DOI
10.1371/journal.pbio.3003539
Method of Research
Experimental study
Subject of Research
Cells
COI Statement
Competing interests: The authors have declared that no competing interests exist.
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Media Contact
Claire Turner
PLOS
biologypress@plos.org
Expert Contact
Lars Barquist
University of Toronto
lars.barquist@utoronto.ca
More on this News Release
Bias in data may be blocking AI’s potential to combat antibiotic resistance
PLOS
Journal
PLOS Biology
DOI
10.1371/journal.pbio.3003539
Keywords
Artificial intelligenceAntibiotic resistanceModeling
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