Article

Biased sampling confounds machine learning prediction of antimicrobial resistance

2025-01-10

Abstract excerpt

Antimicrobial resistance (AMR) poses a growing threat to human health. Increasingly, genome sequencing is being applied for the surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR and identify resistance determinants. However, bacterial populations are highly structured and sampling is biased towards human disease isolates, meaning samples and...

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Literature Corpus work: 970c9d45-0d18-564a-83e5-4585abbd4942
DOI: 10.1101/2025.01.07.631773

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