| 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 | 501 | 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 | 92 | 2018 |
| 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 | 62 | 2021 |
| 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 | 58* | 2022 |
| Adversarial generation of gene expression data R Viñas Torné, H Andrés-Terré, P Lio, K Bryson Oxford University Press (OUP), 2021 | 36* | 2021 |
| The impact of imputation quality on machine learning classifiers for datasets with missing values T Shadbahr, M Roberts, J Stanczuk, J Gilbey, P Teare, S Dittmer, ... Communications Medicine 3 (1), 139, 2023 | 27* | 2023 |
| Deep learning enables fast and accurate imputation of gene expression R Viñas, T Azevedo, ER Gamazon, P Liò Frontiers in genetics 12, 624128, 2021 | 23* | 2021 |
| Hypergraph factorization for multi-tissue gene expression imputation R Viñas, CK Joshi, D Georgiev, P Lin, B Dumitrascu, ER Gamazon, P Liò Nature machine intelligence 5 (7), 739-753, 2023 | 16 | 2023 |
| gRNAde: Geometric Deep Learning for 3D RNA inverse design CK Joshi, AR Jamasb, R Viñas, C Harris, S Mathis, A Morehead, R Anand, ... bioRxiv, 2024.03. 31.587283, 2024 | 7 | 2024 |
| 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 | 5 | 2016 |
| 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 | 4 | 2022 |
| Multi-state rna design with geometric multi-graph neural networks CK Joshi, AR Jamasb, R Viñas, C Harris, S Mathis, P Liò ICML 2023 Workshop on Computation Biology, 2023 | 3 | 2023 |
| 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 | 3 | 2021 |
| NARTI: Neural Algorithmic Reasoning for Trajectory Inference D Georgiev, R Vinas, S Considine, B Dumitrascu, P Lio | 2 | 2023 |
| Attentional Meta-learners for Few-shot Polythetic Classification BJ Day, RV Torné, N Simidjievski, P Lio International Conference on Machine Learning, 4867-4889, 2022 | 2* | 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 | 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 |
| Graph representation learning on tissue-specific multi-omics A Amor, P Lio, V Singh, RV Torné, HA Terre arXiv preprint arXiv:2107.11856, 2021 | 2 | 2021 |
| Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nature Cancer 1 (8)(July 2020) Y Fu, AW Jung, RV Torne, S Gonzalez, H Vöhringer, A Shmatko, L Yates, ... | 2 | |
| 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 | 1 | 2022 |
Pietro LiòProfessor, University of CambridgeVerified email at cam.ac.uk
