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Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data

dc.contributor.authorAstudillo, César A.
dc.contributor.authorLópez-Cortés, Xaviera A.
dc.contributor.authorOcque, Elias
dc.contributor.authorManríquez-Troncoso, José M.
dc.date.accessioned2025-04-10T14:48:41Z
dc.date.available2025-04-10T14:48:41Z
dc.date.issued2024
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/5958
dc.description.abstractAntimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S. aureus, K. pneumoniae, and P. aeruginosa. Using multiple datasets from the DRIAMS repository, we evaluated the performance of four algorithms – Multi-Layer Perceptron, Support Vector Classifier, Random Forest, and Extreme Gradient Boosting – 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.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceScientific Reports, 14, 31283es_CL
dc.subjectAnalytical biochemistryes_CL
dc.subjectBioinformaticses_CL
dc.subjectBiological techniqueses_CL
dc.subjectBiomarkerses_CL
dc.subjectComputational biology and bioinformaticses_CL
dc.subjectData acquisitiones_CL
dc.subjectData integrationes_CL
dc.subjectData mininges_CL
dc.subjectDiagnostic markerses_CL
dc.subjectMachine learninges_CL
dc.subjectMass spectrometryes_CL
dc.subjectPredictive markerses_CL
dc.subjectProteomic analysises_CL
dc.titleMulti-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry dataes_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionIsies_CL
dc.ucm.urinature.ucm.elogim.com/articles/s41598-024-82697-wes_CL
dc.ucm.doidoi.org/10.1038/s41598-024-82697-wes_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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