María Paula Magariños

This study identifies factors governing cysteine detection in chemoproteomics using machine learning and statistical filtering, showing that peptide properties and abundance are key determinants.

This study identifies novel genetic risk variants and potential drug targets for heart failure using GWAS and Mendelian randomization proteomics.

The paper prioritizes 3.7 million patents to identify those likely containing bioactivity data for understudied targets, aiming to curate them for the ChEMBL database.