Barbara Bravi

ANTIPASTI predicts antibody binding affinity using Normal Mode correlation maps and a Convolutional Neural Network, revealing structural and energetic patterns that influence binding.

This paper introduces diffRBM, a transfer-learning model using Restricted Boltzmann Machines to predict antigen immunogenicity and T-cell receptor specificity, achieving performance comparable to existing methods.

The paper benchmarks 23 TCR-epitope prediction models, finding that using paired-chain alpha-beta and CDR1/2/V/J data improves performance, and simple distance-based methods can be competitive.

This paper develops a probabilistic machine learning model to identify antigen-specific TCRs using sequence data and clone abundance, enabling personalized discrimination of specific versus non-specific T-cell responses.

This paper introduces a flexible machine learning method for predicting antigen presentation by HLA-I alleles and shows its improved accuracy for rare alleles.