Belén Rodríguez‐Sánchez

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 study compares subcutaneous and intranasal infection routes of RVFV in sheep, showing differences in disease progression, immune response, and horizontal transmission.

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 study uses FT-IR spectroscopy to rapidly characterize a nosocomial outbreak of VIM-1-producing K. oxytoca, showing high concordance with whole-genome sequencing.

This study uses MALDI-TOF MS and machine learning to classify C. difficile ribotypes, achieving high accuracy for some ribotypes but not all.

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.