{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T08:17:43Z","timestamp":1783498663418,"version":"3.55.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":153,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal State of North Rhine Westfalia","award":["EFRE-20800459"],"award-info":[{"award-number":["EFRE-20800459"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 2500 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioadv\/vbag153","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T11:23:16Z","timestamp":1780312996000},"source":"Crossref","is-referenced-by-count":0,"title":["stackPredAMR\u2014a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8197-076X","authenticated-orcid":false,"given":"Julian","family":"Welling","sequence":"first","affiliation":[{"name":"University of Duisburg-Essen Department of Medicine, , Essen, 45147,","place":["Germany"]},{"name":"University of Duisburg-Essen Department of Computer Science, , Essen, 45127,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2083-5651","authenticated-orcid":false,"given":"Miriam","family":"Balzer","sequence":"additional","affiliation":[{"name":"University of 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