Kolmogorov Regularization for Link Prediction. - Paris D. L. Flood; Ramón Viñas 001; Pietro Liò (CoRR, 2020)

Graph representation forecasting of patient's medical conditions: towards a digital twin. - Pietro Barbiero; Ramón Viñas Torné 001; Pietro Lió (CoRR, 2020)

pietrobarbiero/digital-patient: Absolutno. - Pietro Barbiero; Ramón Viñas Torné 001; Pietro Liò (Zenodo, 2020)

Attentional meta-learners are polythetic classifiers. - Ben Day; Ramón Viñas 001; Nikola Simidjievski; Pietro Liò (CoRR, 2021)

Graph Representation Learning on Tissue-Specific Multi-Omics. - Amine Amor; Pietro Lio'; Vikash Singh; Ramón Viñas Torné 001; Helena Andrés-Terré (CoRR, 2021)

An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution. - James King; Ramón Viñas Torné 001; Alexander Campbell; Pietro Liò (CoRR, 2021)

A Graph-based Imputation Method for Sparse Medical Records. - Ramón Viñas 001; Xu Zheng; Jer Hayes (CoRR, 2021)

Attentional Meta-learners for Few-shot Polythetic Classification. - Ben J. Day; Ramón Viñas Torné 001; Nikola Simidjievski; Pietro Lió (ICML, 2022)

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks. - Arian R. Jamasb; Ramón Viñas Torné 001; Eric Ma; Yuanqi Du; Charles Harris; Kexin Huang; Dominic Hall; Pietro Lió; Tom L. Blundell (NeurIPS, 2022)

Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. - Paul Scherer; Maja Trebacz; Nikola Simidjievski; Ramón Viñas 001; Zohreh Shams; Helena Andrés-Terré; Mateja Jamnik; Pietro Liò (Bioinform., 2022)

Adversarial generation of gene expression data. - Ramón Viñas 001; Helena Andrés-Terré; Pietro Liò; Kevin Bryson 001 (Bioinform., 2022)

Classification of datasets with imputed missing values: does imputation quality matter? - Tolou Shadbahr; Michael Roberts; Jan Stanczuk; Julian D. Gilbey; Philip Teare; Sören Dittmer; Matthew Thorpe; Ramón Viñas Torné 001; Evis Sala; Pietro Lió; Mishal Patel; AIX-COVNET Collaboration; et al. (CoRR, 2022)

Multi-State RNA Design with Geometric Multi-Graph Neural Networks. - Chaitanya K. Joshi; Arian R. Jamasb; Ramón Viñas 001; Charles Harris; Simon V. Mathis; Pietro Liò (CoRR, 2023)

Hypergraph Factorisation Expression Quantitative Trait Loci. - Ramón Viñas 001; Chaitanya K. Joshi; Dobrik Georgiev; Phillip Lin; Bianca Dumitrascu; Pietro Liò; Eric R. Gamazon (Zenodo, 2023)

Hypergraph factorization for multi-tissue gene expression imputation. - Ramón Viñas 001; Chaitanya K. Joshi; Dobrik Georgiev; Phillip Lin; Bianca Dumitrascu; Eric R. Gamazon; Pietro Liò (Nat. Mac. Intell., 2023)

Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease. - Elisa Gómez de Lope; Saurabh Deshpande; Ramón Viñas Torné 001; Pietro Liò; Enrico Glaab; Stéphane P. A. Bordas (CoRR, 2024)

gRNAde: Geometric Deep Learning for 3D RNA inverse design. - Chaitanya K. Joshi; Arian Rokkum Jamasb; Ramón Viñas Torné 001; Charles Harris; Simon V. Mathis; Alex Morehead; Rishabh Anand; Pietro Lio (ICLR, 2025)