Felipe Campelo

This paper improves epitope prediction by using phylogeny-informed transfer learning with protein language models, showing better performance than existing methods.

The paper develops a phylogeny-aware model to predict linear B-cell epitopes for Monkeypox virus, identifying nine specific peptides as potential diagnostic targets.

CALANGO identifies genotype-phenotype associations across species by integrating genomic, evolutionary, and phenotypic data to uncover biologically relevant molecular mechanisms.

This paper investigates how organism-specific training improves epitope prediction performance, even with small datasets, and shows it can outperform generalist models.

This paper presents a multi-objective approach to prioritize epitopes for diagnostic tests, balancing immunogenicity and cross-reactivity, and identifies 20 new potential epitopes across three pathogens.