End-to-end prediction of protein-protein interaction based on embedding and recurrent neural networks. - Francisco Gonzalez-Lopez; Juan Andres Morales-Cordovilla; Amelia Villegas-Morcillo; Angel M. Gomez; Victoria E. Sánchez (BIBM, 2018)

Improved Protein Residue-Residue Contact Prediction Using Image Denoising Methods. - Amelia Villegas-Morcillo; Juan Andres Morales-Cordovilla; Angel M. Gomez; Victoria E. Sánchez (EUSIPCO, 2018)

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function. - Amelia Villegas-Morcillo; Stavros Makrodimitris; Roeland C. H. J. van Ham; Angel M. Gomez; Victoria E. Sánchez; Marcel J. T. Reinders (Bioinform., 2021)

FoldHSphere: deep hyperspherical embeddings for protein fold recognition. - Amelia Villegas-Morcillo; Victoria E. Sánchez; Angel M. Gomez (BMC Bioinform., 2021)

Protein Fold Recognition From Sequences Using Convolutional and Recurrent Neural Networks. - Amelia Villegas-Morcillo; Angel M. Gomez; Juan Andres Morales-Cordovilla; Victoria E. Sánchez (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Fusion of Classical Digital Signal Processing and Deep Learning methods (FTCAPPS). - Ángel M. Gómez; Victoria E. Sánchez; Antonio M. Peinado; Juan M. Martín-Doñas; Alejandro Gómez Alanís; Amelia Villegas-Morcillo; Eros Rosello; Manuel Chica; Celia García; Iván López-Espejo (IberSPEECH, 2022)

An analysis of protein language model embeddings for fold prediction. - Amelia Villegas-Morcillo; Angel M. Gomez; Victoria E. Sánchez (Briefings Bioinform., 2022)

Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models. - Chirag Raman; Alec Nonnemaker; Amelia Villegas-Morcillo; Hayley Hung; Marco Loog (NeurIPS, 2023)

ManyFold: an efficient and flexible library for training and validating protein folding models. - Amelia Villegas-Morcillo; Louis Robinson; Arthur Flajolet; Thomas D. Barrett (Bioinform., 2023)