Matteo Manfredi

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

DDGemb predicts protein stability changes from single- and multi-point variations using embeddings and deep learning, achieving state-of-the-art performance.

CoCoNat predicts coiled-coil boundaries, register annotations, and oligomerization states using deep learning, outperforming existing methods on multiple metrics.

ISPRED-SEQ predicts protein-protein interaction sites from sequences using deep neural networks and outperforms existing methods.

E-SNPs&GO improves pathogenic variant annotation by embedding protein sequences and function, achieving a MCC score of 0.71 on a blind test set.