Integrating machine learning with MALDI-TOF mass spectrometry for rapid and accurate antimicrobial resistance detection in clinical pathogens

López-Cortés, Xaviera A.; Manríquez-Troncoso, José M.; Yáñez Sepúlveda, Alejandra; Suazo Soto, Patricio

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dc.contributor.author	López-Cortés, Xaviera A.
dc.contributor.author	Manríquez-Troncoso, José M.
dc.contributor.author	Yáñez Sepúlveda, Alejandra
dc.contributor.author	Suazo Soto, Patricio
dc.date.accessioned	2025-05-29T18:50:50Z
dc.date.available	2025-05-29T18:50:50Z
dc.date.issued	2025
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/6052
dc.description.abstract	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® 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® 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.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	International Journal of Molecular Sciences, 26(3), 1140	es_CL
dc.subject	Antibiotic resistance	es_CL
dc.subject	Staphylococcus aureus	es_CL
dc.subject	Escherichia coli	es_CL
dc.subject	Klebsiella pneumoniae	es_CL
dc.subject	Machine learning	es_CL
dc.subject	Transfer learning	es_CL
dc.title	Integrating machine learning with MALDI-TOF mass spectrometry for rapid and accurate antimicrobial resistance detection in clinical pathogens	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.uri	mdpi.com/1422-0067/26/3/1140	es_CL
dc.ucm.doi	doi.org/10.3390/ijms26031140	es_CL