| Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer C Blatti*, J de la Fuente*, H Gao, I Marín-Goñi, Z Chen, SD Zhao, W Tan, ... Cancer research 83 (8), 1361-1380, 2023 | 5 | 2023 |
| GeNNius: an ultrafast drug–target interaction inference method based on graph neural networks U Veleiro, J de la Fuente, G Serrano, M Pizurica, M Casals, ... Bioinformatics 40 (1), btad774, 2024 | 2 | 2024 |
| Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration J de la Fuente, S Llorente-Gonzalez, P Fernandez-Robredo, ... Acta Ophthalmologica, 2023 | | 2023 |
| Towards a more inductive world for drug repurposing approaches J de la Fuente Cedeño, G Serrano, U Veleiro, M Casals, L Vera, ... NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development, 2023 | | 2023 |
| Towards a more inductive world for drug repurposing approaches J de la Fuente, G Serrano, U Veleiro, M Casals, L Vera, M Pizurica, ... arXiv preprint arXiv:2311.12670, 2023 | | 2023 |
| Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution J de la Fuente, N Legarra, G Serrano, AG Osta, KR Kalari, ... arXiv preprint arXiv:2311.11991, 2023 | | 2023 |
| Characterization of Transcriptional Alterations Leading to Aberrant Myeloid Differentiation in Myelodysplastic Syndromes A Diaz-Mazkiaran, J De la Fuente, G Serrano, P Garcia-Olloqui, ... Blood 140 (Supplement 1), 5852-5854, 2022 | | 2022 |
Mikel HernaezCenter for Applied Medical Research (CIMA), University of NavarraVerified email at unav.es
