Giancarlo Croce

The paper quantifies selection efficiency across Escherichia coli lineages by linking mutation scores to population genetics, showing significant variation in selection intensity between synonymous and non-synonymous mutations and pathogenic populations.

This study shows that unpaired TCRα + TCRβ sequencing can train accurate TCR-epitope recognition predictors at lower cost than paired sequencing.

The paper quantifies how V and J gene usage affects CDR3 length and amino acid composition in TCR repertoires and provides a quality control framework for TCR sequencing data.

The paper introduces TCR specificity profiles to identify key determinants of TCR-epitope recognition and shows their ability to predict cross-reactivity and interpret machine learning models.

The paper uses phage display and machine learning to discover TCRs specific to cancer antigens, identifying functional TCRs from large repertoires.

This paper introduces MixTCRpred, a deep learning model that predicts TCR-epitope interactions and identifies epitope-specific T cells, showing enrichment in COVID-19 patients.

The paper improves epitope prediction by integrating HLA-I ligand data and TCR recognition models, identifying potent SARS-CoV-2 CD8+ T-cell epitopes with experimental validation.

FilterDCA predicts inter-domain and inter-protein contacts using coevolutionary analysis and interpretable filters, achieving comparable accuracy to deep learning methods while being fully transparent.

The paper predicts unknown protein domain interactions using multi-scale coevolutionary analysis combining phylogenetic and residue-level coevolution data.