Ramon Viñas Torné
Ramon Viñas Torné
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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
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
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
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
Adversarial generation of gene expression data
R Viñas Torné, H Andrés-Terré, P Lio, K Bryson
Oxford University Press (OUP), 2021
Deep learning enables fast and accurate imputation of gene expression
R Viñas, T Azevedo, ER Gamazon, P Liò
Frontiers in Genetics, 489, 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
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
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
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
Attentional Meta-learners for Few-shot Polythetic Classification
BJ Day, RV Torné, N Simidjievski, P Liò
International Conference on Machine Learning, 4867-4889, 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
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
Graph representation learning on tissue-specific multi-omics
A Amor, P Lio, V Singh, RV Torné, HA Terre
arXiv preprint arXiv:2107.11856, 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
Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction
PDL Flood, R Viñas, P Liò
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
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
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
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
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