Leandro A. Bugnon

The paper evaluates cross-validation strategies for RNA secondary structure prediction, showing that current methods bias results and proposing homology-aware strategies for fair comparison.

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.

sincFold is a deep learning model that predicts RNA secondary structure contact matrices from sequences, outperforming existing methods on benchmark datasets.

This paper introduces a convolutional deep residual neural network for predicting pre-miRNAs with high precision across multiple genomes.