Rosario Vitale

LLPSight: enhancing prediction of LLPS-driving proteins using machine learning and protein Language Models

LLPSight predicts LLPS-driving proteins using machine learning and protein language models, achieving an F1 score of 0.885 and identifying promising targets in the human proteome.

GNN2Pfam: Integrating protein sequence and structure with graph neural networks for Pfam domain annotation

GNN2Pfam uses graph neural networks to improve Pfam domain annotation by integrating protein sequence and structure data, outperforming HMM-based methods.

RNAcomp2D: a web tool to execute multiple methods and visually compare predictions of RNA secondary structure

RNAcomp2D provides a web tool to run multiple RNA secondary structure prediction methods and compare their results visually.

ET-Pfam: Ensemble transfer learning for protein family prediction

ET-Pfam improves protein family prediction by combining transfer learning and ensemble strategies, achieving lower error rates than individual models and competitors.