Maurizio Zanetti

This paper introduces NEMo, a machine learning model that predicts neoantigen immunogenicity using in vivo immune editing data, showing improved stratification of progression-free survival across multiple clinical settings.

NeoPrecis improves immunotherapy response prediction by integrating immunogenicity and clonality-aware neoantigen landscapes, showing better performance than tumor mutation burden in melanoma and NSCLC.

This paper uses machine learning to predict ICB response by integrating germline and somatic features, revealing differences in immune pathways and clinical outcomes between MHC-I and MHC-II neoantigen reliant tumors.

The paper shows that neopeptide immunogenicity is influenced by the subcellular location of their parent proteins, improving predictions of vaccine and immunotherapy responses.

The paper examines how the N501Y mutation in SARS-CoV-2 affects MHC-II presentation, suggesting it may reduce immune response and contribute to immune escape.