Péter Ferdinandy

Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance

This paper develops interpretable adverse event vector representations using contrastive learning for pharmacovigilance, outperforming existing methods in drug-event causality prediction.

Assessment of community efforts to advance computational prediction of protein-protein interactions

The paper evaluates 24 network-based methods for predicting protein-protein interactions across five interactomes, identifies top-performing methods, and validates their predictions experimentally.