Carlos Sevilla-Salcedo

This review evaluates ML applications for MALDI-TOF MS in clinical microbiology, identifying trends and challenges in bacterial identification, resistance prediction, and strain typing.

This paper develops automated tools using MALDI-TOF MS and machine learning to accurately classify C. difficile ribotypes from clinical isolates.

The paper introduces a large MALDI-TOF MS dataset from Spain to support AI-driven clinical microbiology research and improve antimicrobial resistance detection.

This paper introduces an unsupervised method for detecting novel Escherichia coli strains using MALDI-TOF MS data and machine learning techniques.

This study improves ML-based C. difficile typing from MALDI-TOF MS spectra by using data augmentation to reduce performance variability across different experimental conditions.

The paper introduces a feature selection and extraction method for multiclass brain imaging problems, reducing feature dimensions while improving classification performance.