{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T13:36:47Z","timestamp":1791639407820,"version":"4.3.4"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T00:00:00Z","timestamp":1738022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002848","name":"ANID Chile, FONDECYT Iniciaci\u00f3n en Investigaci\u00f3n","doi-asserted-by":"publisher","award":["11220897"],"award-info":[{"award-number":["11220897"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Antimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of mass spectral data generated by VITEK\u00ae MS instruments for predicting antibiotic resistance in Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae using machine learning algorithms. Additionally, the potential of pre-trained models was assessed through transfer learning analysis. A dataset comprising 2229 mass spectra was collected, and classification algorithms, including Support Vector Machines, Random Forest, Logistic Regression, and CatBoost, were applied to predict resistance. CatBoost demonstrated a clear advantage over the other models, effectively handling complex non-linear relationships within the spectra and achieving an AUROC of 0.91 and an F1 score of 0.78 for E. coli. In contrast, transfer learning yielded suboptimal results. These findings highlight the potential of gradient-boosting techniques to enhance resistance prediction, particularly with data from less conventional platforms like VITEK\u00ae MS. Furthermore, the identification of specific biomarkers using SHAP values indicates promising potential for clinical applications in early diagnosis. Future efforts focused on standardizing data and refining algorithms could expand the utility of these approaches across diverse clinical environments, supporting the global fight against AMR.<\/jats:p>","DOI":"10.3390\/ijms26031140","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T06:47:29Z","timestamp":1738046849000},"page":"1140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7514-8777","authenticated-orcid":false,"given":"Xaviera A.","family":"L\u00f3pez-Cort\u00e9s","sequence":"first","affiliation":[{"name":"Departamento de Computaci\u00f3n e Industrias, Facultad de Ciencias de la Ingenier\u00eda, Universidad Cat\u00f3lica del Maule, Talca 3460000, Chile"},{"name":"Centro de Innovaci\u00f3n en Ingenier\u00eda Aplicada (CIIA), Universidad Cat\u00f3lica del Maule, Talca 3460000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 M.","family":"Manr\u00edquez-Troncoso","sequence":"additional","affiliation":[{"name":"Departamento de Computaci\u00f3n e Industrias, Facultad de Ciencias de la Ingenier\u00eda, Universidad Cat\u00f3lica del Maule, Talca 3460000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandra Y\u00e1\u00f1ez","family":"Sep\u00falveda","sequence":"additional","affiliation":[{"name":"Departamento de Computaci\u00f3n e Industrias, Facultad de Ciencias de la Ingenier\u00eda, Universidad Cat\u00f3lica del Maule, Talca 3460000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9214-5666","authenticated-orcid":false,"given":"Patricio Suazo","family":"Soto","sequence":"additional","affiliation":[{"name":"Departamento de Microbiolog\u00eda, Facultad de Ciencias de la Salud, Universidad de Talca, Campus Talca, Avda. Lircay s\/n, Talca 3460000, Chile"},{"name":"Centro de Ecolog\u00eda Integrativa, Universidad de Talca, Campus Talca, Avda. Lircay s\/n, Talca 3460000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023). Vaccines Could Avert Half a Million Deaths Associated with Anti-Microbial Resistance A Year, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3903","DOI":"10.2147\/IDR.S234610","article-title":"Antimicrobial Resistance: Implications and Costs","volume":"12","author":"Dadgostar","year":"2019","journal-title":"Infect. Drug Resist."},{"key":"ref_3","unstructured":"Instituto de Salud P\u00fablica de Chile (2022). Bolet\u00edn de Resistencia Antimicrobiana, Instituto de Salud P\u00fablica de Chile."},{"key":"ref_4","unstructured":"Instituto de Salud P\u00fablica de Chile (2019). Informe de Vigilancia de Infecciones Asociadas a la Atenci\u00f3n en Salud, Instituto de Salud P\u00fablica de Chile."},{"key":"ref_5","unstructured":"World Health Organization (2002). Promoting Rational Use of Medicines, World Health Organization."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"266","DOI":"10.5187\/jast.2023.e129","article-title":"Antibiotic resistance in livestock, environment and humans: One Health perspective","volume":"66","author":"Pandey","year":"2024","journal-title":"J. Anim. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113267","DOI":"10.1016\/j.envres.2022.113267","article-title":"Detection and various environmental factors of antibiotic resistance gene horizontal transfer","volume":"212","author":"Li","year":"2022","journal-title":"Environ. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13750-022-00262-2","article-title":"Existing evidence on antibiotic resistance exposure and transmission to humans from the environment: A systematic map","volume":"11","author":"Stanton","year":"2022","journal-title":"Environ. Evid."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Iskandar, K., Murugaiyan, J., Hammoudi Halat, D., Hage, S.E., Chibabhai, V., Adukkadukkam, S., Roques, C., Molinier, L., Salameh, P., and Van Dongen, M. (2022). Antibiotic discovery and resistance: The chase and the race. Antibiotics, 11.","DOI":"10.3390\/antibiotics11020182"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e24655","DOI":"10.1002\/jcla.24655","article-title":"Antibiotic resistance: The challenges and some emerging strategies for tackling a global menace","volume":"36","author":"Ugwu","year":"2022","journal-title":"J. Clin. Lab. Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pinto-de S\u00e1, R., Sousa-Pinto, B., and Costa-de Oliveira, S. (2024). Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship\u2014A Systematic Review. Antibiotics, 13.","DOI":"10.3390\/antibiotics13040307"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Coxe, T., and Azad, R.K. (2023). Silicon versus Superbug: Assessing Machine Learning\u2019s Role in the Fight against Antimicrobial Resistance. Antibiotics, 12.","DOI":"10.3390\/antibiotics12111604"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/s41586-023-06887-8","article-title":"Discovery of a structural class of antibiotics with explainable deep learning","volume":"626","author":"Wong","year":"2023","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Van Camp, P.J., Haslam, D.B., and Porollo, A. (2020). Prediction of antimicrobial resistance in Gram-negative bacteria from whole-genome sequencing data. Front. Microbiol., 11.","DOI":"10.3389\/fmicb.2020.01013"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Meng, L., Liu, H., Lan, T., Dong, L., Hu, H., Zhao, S., Zhang, Y., Zheng, N., and Wang, J. (2020). Antibiotic resistance patterns of Pseudomonas spp. isolated from raw milk revealed by whole genome sequencing. Front. Microbiol., 11.","DOI":"10.3389\/fmicb.2020.01005"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.eimc.2020.02.027","article-title":"MALDI-TOF mass spectrometry in the 21st century clinical microbiology laboratory","volume":"39","year":"2021","journal-title":"Enfermedades Infecc. Microbiol. Clin. (Engl. Ed.)"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tsuchida, S., and Nakayama, T. (2022). MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification. Appl. Sci., 12.","DOI":"10.3390\/app12062814"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Janiszewska, D., Szultka-M\u0142y\u0144ska, M., Pomastowski, P., and Buszewski, B. (2022). \u201cOmic\u201d Approaches to Bacteria and Antibiotic Resistance Identification. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23179601"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yoon, E.J., and Jeong, S.H. (2021). MALDI-TOF Mass Spectrometry Technology as a Tool for the Rapid Diagnosis of Antimicrobial Resistance in Bacteria. Antibiotics, 10.","DOI":"10.3390\/antibiotics10080982"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chung, C.R., Wang, H.Y., Chou, P.H., Wu, L.C., Lu, J.J., Horng, J.T., and Lee, T.Y. (2023). Towards accurate identification of antibiotic-resistant pathogens through the ensemble of multiple preprocessing methods based on MALDI-TOF spectra. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24020998"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Astudillo, C.A., L\u00f3pez-Cort\u00e9s, X.A., Ocque, E., and Manr\u00edquez-Troncoso, J.M. (2024). Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-82697-w"},{"key":"ref_22","unstructured":"CLSI (2023). Performance Standards for Antimicrobial Susceptibility Testing, Clinical and Laboratory Standards Institute (CLSI). [33rd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"465262","DOI":"10.1016\/j.chroma.2024.465262","article-title":"Machine learning and matrix-assisted laser desorption\/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development","volume":"1734","year":"2024","journal-title":"J. Chromatogr. A"},{"key":"ref_24","first-page":"215","article-title":"Recent deep learning methods for tabular data","volume":"30","author":"Hwang","year":"2023","journal-title":"Commun. Stat. Appl. Methods"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Cort\u00e9s, X.A., Manr\u00edquez-Troncoso, J.M., Hern\u00e1ndez-Garc\u00eda, R., and Peralta, D. (2024). MSDeepAMR: Antimicrobial resistance prediction based on deep neural networks and transfer learning. Front. Microbiol., 15.","DOI":"10.3389\/fmicb.2024.1361795"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1038\/s41591-021-01619-9","article-title":"Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning","volume":"28","author":"Weis","year":"2022","journal-title":"Nat. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9381","DOI":"10.1007\/s00521-023-08333-2","article-title":"Semi-supervised learning for MALDI\u2013TOF mass spectrometry data classification: An application in the salmon industry","volume":"35","author":"Astudillo","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e03479-22","DOI":"10.1128\/spectrum.03479-22","article-title":"Data-driven two-stage framework for identification and characterization of different antibiotic-resistant escherichia coli isolates based on mass spectrometry data","volume":"11","author":"Chung","year":"2023","journal-title":"Microbiol. Spectr."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, Z., Wang, H.Y., Chung, C.R., Horng, J.T., Lu, J.J., and Lee, T.Y. (2022). Large-scale samples based rapid detection of ciprofloxacin resistance in Klebsiella pneumoniae using machine learning methods. Front. Microbiol., 13.","DOI":"10.3389\/fmicb.2022.827451"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kim, J.M., Kim, I., Chung, S.H., Chung, Y., Han, M., and Kim, J.S. (2019). Rapid discrimination of methicillin-resistant Staphylococcus aureus by MALDI-TOF MS. Pathogens, 8.","DOI":"10.3390\/pathogens8040214"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, H.Y., Lee, T.Y., Tseng, Y.J., Liu, T.P., Huang, K.Y., Chang, Y.T., Chen, C.H., and Lu, J.J. (2018). A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194289"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.mimet.2015.08.002","article-title":"Analysis of methicillin-resistant Staphylococcus aureus major clonal lineages by matrix-assisted laser desorption ionization\u2013time of flight mass spectrometry (MALDI\u2013TOF MS)","volume":"117","author":"Zhang","year":"2015","journal-title":"J. Microbiol. Methods"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e28","DOI":"10.1016\/S1473-3099(20)30868-9","article-title":"The urgent need for metallo-\u03b2-lactamase inhibitors: An unattended global threat","volume":"22","author":"Mojica","year":"2022","journal-title":"Lancet Infect. Dis."},{"key":"ref_34","unstructured":"Moller, A. (2021). Understanding Phage-Host Interactions in Staphylococcus aureus Through Population Genomics and Bioinformatics. [Ph.D. Thesis, Emory University]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nakamura, A., Komatsu, M., Ohno, Y., Noguchi, N., Kondo, A., and Hatano, N. (2019). Identification of specific protein amino acid substitutions of extended-spectrum \u03b2-lactamase (ESBL)-producing Escherichia coli ST131: A proteomics approach using mass spectrometry. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-45051-z"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1097\/CM9.0000000000001101","article-title":"Genetic factors related to the widespread dissemination of ST11 extensively drug-resistant carbapenemase-producing Klebsiella pneumoniae strains within hospital","volume":"133","author":"Li","year":"2020","journal-title":"Chin. Med J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100121","DOI":"10.1016\/j.microb.2024.100121","article-title":"The Silent Architect: Nucleoid Associated Proteins\u2019 Influence on the ESKAPE Pathogens","volume":"4","author":"Panda","year":"2024","journal-title":"Microbe"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Giamarellou, H., Galani, L., Karavasilis, T., Ioannidis, K., and Karaiskos, I. (2023). Antimicrobial stewardship in the hospital setting: A narrative review. Antibiotics, 12.","DOI":"10.3390\/antibiotics12101557"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e30","DOI":"10.26633\/RPSP.2020.30","article-title":"Socioeconomic factors associated with antimicrobial resistance of Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli in Chilean hospitals (2008\u20132017)","volume":"44","author":"Allel","year":"2020","journal-title":"Rev. Panam. Salud P\u00fablica"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Oliveira, M., Antunes, W., Mota, S., Madureira-Carvalho, \u00c1., Dinis-Oliveira, R.J., and da Silva, D.D. (2024). An Overview of the Recent Advances in Antimicrobial Resistance. Microorganisms, 12.","DOI":"10.3390\/microorganisms12091920"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/3\/1140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:37:49Z","timestamp":1759919869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/3\/1140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,28]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["ijms26031140"],"URL":"https:\/\/doi.org\/10.3390\/ijms26031140","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,28]]}}}