{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:11Z","timestamp":1787008451850,"version":"build-2736575974"},"reference-count":169,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Ministry of Science, Innovation and Universities\u2014Agencia Estatal de Investigaci\u00f3n","award":["MCIN\/AEI\/10.13039\/501100011033\/FEDER"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033\/FEDER"]}]},{"name":"Ministry of Science, Innovation and Universities\u2014Agencia Estatal de Investigaci\u00f3n","award":["PID2023-146684NB-I00"],"award-info":[{"award-number":["PID2023-146684NB-I00"]}]},{"name":"the Community of Madrid","award":["TEC-2024\/COM-89"],"award-info":[{"award-number":["TEC-2024\/COM-89"]}]},{"name":"European Union\u2019s Horizon Europe programme"},{"name":"Marie Sk\u0142odowska-Curie Actions Postdoctoral Fellowship","award":["101278403"],"award-info":[{"award-number":["101278403"]}]},{"name":"Comunidad de Madrid\u2019s 2025 Cesar Nombela program","award":["2025-T1\/COM-36091"],"award-info":[{"award-number":["2025-T1\/COM-36091"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Bacterial identification, antimicrobial resistance prediction, and strain typification are critical tasks in clinical microbiology, essential for guiding patient treatment and controlling the spread of infectious diseases. While machine learning (ML) has shown immense promise in enhancing Matrix-Assisted Laser Desorption\/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) applications for these tasks, there is currently no comprehensive review that fully addresses this from a technical ML perspective. To address this gap, we systematically reviewed 115 studies published between 2004 and 2025, focusing on key ML aspects such as data size and balance, preprocessing pipelines, model selection and evaluation, open-source data, and code availability. Our analysis highlights the predominant use of classical ML models like Random Forest and Support Vector Machines, alongside emerging interest in deep learning approaches for handling complex, high-dimensional data. Despite significant progress, challenges such as inconsistent preprocessing workflows, reliance on black-box models, limited external validation, and insufficient open-source resources persist, hindering transparency, reproducibility, and broader adoption. This review offers actionable insights to enhance ML-driven bacterial diagnostics, advocating for standardized methodologies, greater transparency, and improved data accessibility. In addition, we provide guidelines on how to approach ML for MALDI-TOF MS analysis, helping researchers navigate key decisions in model development and evaluation.<\/jats:p>","DOI":"10.1093\/bib\/bbag208","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T13:34:26Z","timestamp":1776432866000},"source":"Crossref","is-referenced-by-count":1,"title":["A systematic review of machine learning on clinical MALDI-TOF MS"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5754-4441","authenticated-orcid":false,"given":"Luc\u00eda","family":"Schmidt-Santiago","sequence":"first","affiliation":[{"name":"Department of Signal Theory and Communications, Universidad Carlos III de Madrid , Avda. Universidad, 30, E-28911 Legan\u00e9s,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8869-3363","authenticated-orcid":false,"given":"Alejandro","family":"Guerrero-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Institute for Medical Microbiology, University of Zurich , Gloriastrasse 30, CH-8006 Zurich,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-7537","authenticated-orcid":false,"given":"Carlos","family":"Sevilla-Salcedo","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, Universidad Carlos III de Madrid , Avda. Universidad, 30, E-28911 Legan\u00e9s,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1299-3110","authenticated-orcid":false,"given":"David","family":"Rodr\u00edguez-Temporal","sequence":"additional","affiliation":[{"name":"Department of Clinical Microbiology and Infectious Diseases, Gregorio Mara\u00f1\u00f3n Health Research Institute, Hospital General Universitario Gregorio Mara\u00f1\u00f3n , C. del Dr. Esquerdo 46, E-28007 Madrid,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9625-427X","authenticated-orcid":false,"given":"Bel\u00e9n","family":"Rodr\u00edguez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Clinical Microbiology and Infectious Diseases, Gregorio Mara\u00f1\u00f3n Health Research Institute, Hospital General Universitario Gregorio Mara\u00f1\u00f3n , C. del Dr. Esquerdo 46, E-28007 Madrid,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7702-8747","authenticated-orcid":false,"given":"Vanessa","family":"G\u00f3mez-Verdejo","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, Universidad Carlos III de Madrid , Avda. Universidad, 30, E-28911 Legan\u00e9s,","place":["Spain"]},{"name":"Signal Processing Group, Gregorio Mara\u00f1\u00f3n Health Research Institute, Hospital General Universitario Gregorio Mara\u00f1\u00f3n , C. del Dr. Esquerdo 46, E-28007 Madrid,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"2026050811183357200_ref1","doi-asserted-by":"crossref","first-page":"i2","DOI":"10.1093\/jac\/dkae275","article-title":"Evolving strategies in microbe identification\u2014a comprehensive review of biochemical, MALDI-TOF MS and molecular testing methods","volume":"79","author":"Arbefeville","year":"2024","journal-title":"J Antimicrob Chemother"},{"key":"2026050811183357200_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/ice.2019.296","article-title":"Antimicrobial-resistant pathogens associated with adult healthcare-associated infections: summary of data reported to the 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augmentation","author":"Guerrero-L\u00f3pez","year":"2024","journal-title":"bioRxiv."},{"key":"2026050811183357200_ref34","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10462-024-10916-x","article-title":"Explainable generative AI (genXAI): a survey, conceptualization, and research agenda","volume":"57","author":"Schneider","year":"2024","journal-title":"Artif Intell Rev"},{"key":"2026050811183357200_ref35","unstructured":"Weis C, Cu\u00e9nod A, Rieck B \u00a0et\u00a0al. \u00a0DRIAMS: database of resistance information on antimicrobials and MALDI-TOF mass spectra. 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