Vanessa Gómez-Verdejo

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 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.

This paper introduces sign-consistency bagging (SCB) for variable importance in brain imaging, showing improved reproducibility and classification accuracy compared to existing methods.

This study compares MRI-based feature representations for predicting MCI-to-AD conversion, finding regional features perform best but without significant statistical differences.