Romina Oliva

The paper improves protein-protein docking model classification by augmenting the training set with a weakly supervised method, achieving higher accuracy and MCC than existing scoring functions.

The paper develops a Random Forest classifier, CoDES, to distinguish correct from incorrect protein-protein docking models using machine learning and scoring functions.

The paper identifies mutations in the SARS-CoV-2 spike protein's fusion core that disrupt post-fusion assembly, with D936Y showing increased prevalence and weakening interactions.