| Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis Y Fu, AW Jung, RV Torne, S Gonzalez, H Vöhringer, A Shmatko, LR Yates, ... Nature cancer 1 (8), 800-810, 2020 | 304 | 2020 |
| Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction B López, F Torrent-Fontbona, R Viñas, JM Fernández-Real Artificial intelligence in medicine 85, 43-49, 2018 | 80 | 2018 |
| Graphein-a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks AR Jamasb, RV Torné, EJ Ma, Y Du, C Harris, K Huang, D Hall, P Lio, ... NeurIPS 2022, 2022 | 33* | 2022 |
| Graph representation forecasting of patient's medical conditions: Toward a digital twin P Barbiero, R Vinas Torne, P Lió Frontiers in genetics 12, 652907, 2021 | 20 | 2021 |
| Adversarial generation of gene expression data R Viñas Torné, H Andrés-Terré, P Lio, K Bryson Oxford University Press (OUP), 2021 | 13* | 2021 |
| Deep learning enables fast and accurate imputation of gene expression R Viñas, T Azevedo, ER Gamazon, P Liò Frontiers in Genetics, 489, 2021 | 11* | 2021 |
| Handling missing phenotype data with random forests for diabetes risk prognosis B López Ibáñez, R Vinas, F Torrent-Fontbona, JM Fernández-Real Lemos © López, B., Herrero, P., Martin, C.(eds).(2016). AID: Artificial …, 2016 | 3 | 2016 |
| A graph-based imputation method for sparse medical records R Viñas, X Zheng, J Hayes Multimodal AI in healthcare: A paradigm shift in health intelligence, 377-385, 2022 | 2 | 2022 |
| Classification of datasets with imputed missing values: Does imputation quality matter? T Shadbahr, M Roberts, J Stanczuk, J Gilbey, P Teare, S Dittmer, ... arXiv preprint arXiv:2206.08478, 2022 | 2 | 2022 |
| An investigation of pre-upsampling generative modelling and generative adversarial networks in audio super resolution J King, RV Torné, A Campbell, P Liò arXiv preprint arXiv:2109.14994, 2021 | 2 | 2021 |
| Attentional Meta-learners for Few-shot Polythetic Classification BJ Day, RV Torné, N Simidjievski, P Liò International Conference on Machine Learning, 4867-4889, 2022 | 1 | 2022 |
| Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases P Scherer, M Trębacz, N Simidjievski, R Viñas, Z Shams, HA Terre, ... Bioinformatics 38 (5), 1320-1327, 2022 | 1 | 2022 |
| Improving Classification and Data Imputation for Single-Cell Transcriptomics with Graph Neural Networks HB Li, RV Torné, P Lio NeurIPS 2022 AI for Science: Progress and Promises, 2022 | 1* | 2022 |
| Graph representation learning on tissue-specific multi-omics A Amor, P Lio, V Singh, RV Torné, HA Terre arXiv preprint arXiv:2107.11856, 2021 | 1 | 2021 |
| Discovering cancer driver genes and pathways using stochastic block model graph neural networks V Fanfani, RV Torne, P Lio’, G Stracquadanio bioRxiv, 2021.06. 29.450342, 2021 | 1 | 2021 |
| Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction PDL Flood, R Viñas, P Liò | 1 | 2020 |
| Multi-State RNA Design with Geometric Multi-Graph Neural Networks CK Joshi, AR Jamasb, R Viñas, C Harris, S Mathis, P Liò arXiv preprint arXiv:2305.14749, 2023 | | 2023 |
| Spatio-relational inductive biases in spatial cell-type deconvolution R Vinas, P Scherer, N Simidjievski, M Jamnik, P Lio bioRxiv, 2023.05. 19.541474, 2023 | | 2023 |
| Hypergraph factorisation for multi-tissue gene expression imputation R Viñas, CK Joshi, D Georgiev, B Dumitrascu, ER Gamazon, P Liò bioRxiv, 2022.07. 31.502211, 2022 | | 2022 |
| Benchmarking Graph Neural Network-based Imputation Methods on Single-Cell Transcriptomics Data HB Li, RV Torné, P Lio NeurIPS 2022 Workshop on Learning Meaningful Representations of Life, 2022 | | 2022 |
P. Lió, Pietro Lio, P. Lio, Pietro Liò, Pie...University of CambridgeVerified email at cam.ac.uk
