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J. Microbiol."],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:p> With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML) models show promise for AMR prediction in diagnostics but require a deep understanding of internal processes to use effectively. Our study utilised AMR gene, pangenomic, and predicted plasmid features from 647 Enterococcus faecium and Enterococcus faecalis genomes across the One Health continuum, along with corresponding resistance phenotypes, to develop interpretive ML classifiers. Vancomycin resistance could be predicted with 99% accuracy with AMR gene features, 98% with pangenome features, and 96% with plasmid clusters. Top pangenome features overlapped with the resistance genes of the vanA operon, which are often laterally transmitted via plasmids. Doxycycline resistance prediction achieved approximately 92% accuracy with pangenome features, with the top feature being elements of Tn 916 conjugative transposon, a tet(M) carrier. Erythromycin resistance prediction models achieved about 90% accuracy, but top features were negatively correlated with resistance due to the confounding effect of population structure. This work demonstrates the importance of reviewing ML models\u2019 features to discern biological relevance even when achieving high-performance metrics. Our workflow offers the potential to propose hypotheses for experimental testing, enhancing the understanding of AMR mechanisms, which are crucial for combating the AMR crisis. <\/jats:p>","DOI":"10.1139\/cjm-2024-0049","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T21:34:55Z","timestamp":1722375295000},"page":"446-460","update-policy":"https:\/\/doi.org\/10.1139\/csp-crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Identification of key drivers of antimicrobial resistance in <i>Enterococcus<\/i> using machine learning"],"prefix":"10.1139","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1729-4474","authenticated-orcid":false,"given":"Jee In","family":"Kim","sequence":"first","affiliation":[{"name":"Dalhousie University","place":["Halifax, Canada"]},{"name":"Institute for Comparative Genomics, Dalhousie University","place":["Halifax, Canada"]},{"name":"Agriculture and Agri-Food 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