S. Duarte

emb2dis predicts protein disorder using a deep learning model combining ResNets and dilated convolutions, achieving top performance on benchmark datasets.

GNN2Pfam uses graph neural networks to improve Pfam domain annotation by integrating protein sequence and structure data, outperforming HMM-based methods.

ET-Pfam improves protein family prediction by combining transfer learning and ensemble strategies, achieving lower error rates than individual models and competitors.