Juan Ángel Fresno Vara

The paper uses metabolomics and gene expression data to identify glutamine metabolism as important in breast cancer and shows drug targeting affects cell viability.

This paper uses computational analyses on TCGA data to classify muscle-invasive bladder cancer into Luminal, Basal, and immune-high groups, suggesting targeted therapies for each.

The paper characterizes breast cancer cell responses to metabolic drugs using proteomics and computational models, showing heterogeneous effects and pathway-level flux changes.

The paper uses probabilistic graphical models on gene expression data from breast cancer patients to show that immune-related functions and markers are more active in tumors that respond completely to neoadjuvant chemotherapy.

The paper uses proteomics and computational methods to identify molecular differences between BRAF-mutated and BRAF non-mutated melanoma tumors.