stackPredAMR – A stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents

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ID: 315762
2026
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Abstract
Abstract Motivation Antimicrobial resistance is a growing global threat, creating a need for rapid and accurate antimicrobial susceptibility testing. Current phenotypic antimicrobial susceptibility testing methods rely on prior isolation and cultivation, making them time-consuming. Whole genome sequencing combined with machine learning offers a faster and cost-effective alternative, but existing approaches are often limited in species coverage, antimicrobial scope, or data availability. Results We developed stackPredAMR, a machine learning framework for predicting resistance to 18 antimicrobial agents in three clinically important bacterial species: Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii. The model uses antimicrobial resistance gene presence as input and incorporates cross-resistance patterns through a stacked architecture with two random forest layers. Benchmarking on more than 2,500 publicly available whole genome sequencing datasets with linked phenotypic resistance data showed strong performance, achieving a median accuracy of 0.94, ROC AUC of 0.97, and F1-score of 0.91, outperforming previously published methods. stackPredAMR is freely available and designed to support future extension to additional species and antimicrobial agents. Availability and implementation Source code and datasets (database-driven reference approach, sample lists and input features) are available at WIN-KID repository (https://github.com/IKIM-Essen/WIN-KID/tree/v1.0.0.0) and the release page (https://github.com/IKIM-Essen/WIN-KID/releases/tag/v1.0.0.0).
Reference Key
openalex_W7163396727 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Julian Welling, Miriam Balzer, Leah Consten, Stefan Bletz, Jan Buer, Valerie Chapot, Dag Harmsen, Evelyn Heintschel von Heinegg, Mellmann Alexander, Wolfgang Pölking, Friederike Salhöfer, Frieder Schaumburg, Natalie Scherff, Niklas Wiesmann, Folker Meyer
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag153
URL
https://doi.org/10.1093/bioadv/vbag153
Keywords Keywords not found

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