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Article

Towards an interpretable machine learning model for predicting antimicrobial resistance.

Journal of global antimicrobial resistance - 1 Dec 2025

Mediouni Mohamed, Makarenkov Vladimir, Diallo Abdoulaye Baniré

Abstract excerpt

This article explores the main stages of developing an interpretable machine learning (ML) model for predicting antimicrobial resistance (AMR), highlighting the importance of model interpretability in enhancing the prediction performance. By integrating phenotype-genotype synergy, our goal is to better understand AMR mechanisms. Such an approach combines ML with biological insights, offering a pathway towards...

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PMID: 40865765
DOI: 10.1016/j.jgar.2025.08.011

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