Chiara Rodella

H3BERTa: A CDR-H3 specific language model for antibody repertoire analysis

H3BERTa is a transformer model trained on CDR-H3 sequences to analyze antibody repertoires, capturing immunological signatures and supporting classifier development for broadly neutralizing antibodies.

Deep generative modeling reveals maturation-linked pairing signatures in human antibodies

This paper develops a deep learning framework to generate light chains from heavy chains using unpaired antibody sequences, revealing maturation-linked pairing patterns.

TemBERTure: Advancing protein thermostability prediction with Deep Learning and attention mechanisms

This paper introduces TemBERTure, a Deep Learning framework for predicting protein thermostability using sequence data, showing improved performance with diverse datasets and attention mechanisms.