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In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: <jats:italic>E. coli<\/jats:italic>, <jats:italic>S. aureus<\/jats:italic>, <jats:italic>K. pneumoniae<\/jats:italic>, and <jats:italic>P. aeruginosa<\/jats:italic>. Using multiple datasets from the DRIAMS repository, we evaluated the performance of four algorithms \u2013 Multi-Layer Perceptron, Support Vector Classifier, Random Forest, and Extreme Gradient Boosting \u2013 under both single-label and multi-label frameworks. Our results demonstrate that the multi-label approach delivers competitive performance compared to traditional single-label models, with no statistically significant differences in most cases. The multi-label framework naturally captures the complex, interconnected nature of AMR data, reflecting real-world scenarios more accurately. We further validated the models on external datasets (DRIAMS B and C), confirming their generalizability and robustness. Additionally, we investigated the impact of oversampling techniques and provided a reproducible methodology for handling MALDI-TOF data, ensuring scalability for future studies. These findings underscore the potential of multi-label classification to enhance predictive accuracy in AMR research, offering valuable insights for developing diagnostic tools and guiding clinical interventions.<\/jats:p>","DOI":"10.1038\/s41598-024-82697-w","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T07:51:49Z","timestamp":1735372309000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data"],"prefix":"10.1038","volume":"14","author":[{"given":"C\u00e9sar A.","family":"Astudillo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xaviera A.","family":"L\u00f3pez-Cort\u00e9s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elias","family":"Ocque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 M.","family":"Manr\u00edquez-Troncoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"82697_CR1","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1136\/MEDHUM-2022-012409","volume":"48","author":"K Hansson","year":"2022","unstructured":"Hansson, K. & Brenthel, A. 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