{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T08:44:50Z","timestamp":1782981890748,"version":"3.54.5"},"reference-count":96,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Cluster of Excellence RESIST"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["390874280"],"award-info":[{"award-number":["390874280"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German Center for Infection Research (DZIF) Translational Infrastructure Bioresources"},{"name":"Digital Health","award":["TI 12.002"],"award-info":[{"award-number":["TI 12.002"]}]},{"name":"NFDI4Microbiota"},{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","award":["460129525"],"award-info":[{"award-number":["460129525"]}],"id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species\u2013antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species\u2013antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin\/clavulanic acid, cefoxitin, ceftazidime and piperacillin\/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species\u2013antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.<\/jats:p>","DOI":"10.1093\/bib\/bbae206","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T20:42:49Z","timestamp":1713300169000},"source":"Crossref","is-referenced-by-count":45,"title":["Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes"],"prefix":"10.1093","volume":"25","author":[{"given":"Kaixin","family":"Hu","sequence":"first","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit\u00e4t Braunschweig , Braunschweig , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fernando","family":"Meyer","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit\u00e4t Braunschweig , Braunschweig , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Luo","family":"Deng","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit\u00e4t Braunschweig , Braunschweig , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsaneddin","family":"Asgari","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Molecular Cell Biomechanics Laboratory , Department of Bioengineering and Mechanical Engineering, , Berkeley , USA"},{"name":"University of California , Department of Bioengineering and Mechanical Engineering, , Berkeley , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzu-Hao","family":"Kuo","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit\u00e4t Braunschweig , Braunschweig , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp C","family":"M\u00fcnch","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit\u00e4t Braunschweig , Braunschweig , Germany"},{"name":"Cluster of Excellence RESIST (EXC 2155), Hannover Medical School , Hannover , Germany"},{"name":"German Center for Infection Research (DZIF), partner site Hannover Braunschweig , Braunschweig , Germany"},{"name":"Department of Biostatistics, Harvard School of Public Health , Boston, MA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice C","family":"McHardy","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research , Braunschweig , Germany"},{"name":"Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universit\u00e4t Braunschweig , Braunschweig , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,5,5]]},"reference":[{"key":"2024050606152628200_ref1","first-page":"356","article-title":"Sequencing-based methods and resources to study 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