| Variational autoencoders for cancer data integration: design principles and computational practice N Simidjievski, C Bodnar, I Tariq, P Scherer, H Andres Terre, Z Shams, ... Frontiers in genetics 10, 1205, 2019 | 104 | 2019 |
| Factorised neural relational inference for multi-interaction systems E Webb, B Day, H Andres-Terre, P Lió arXiv preprint arXiv:1905.08721, 2019 | 24 | 2019 |
| CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks D Buterez, I Bica, I Tariq, H Andrés-Terré, P Liò Bioinformatics 38 (5), 1277-1286, 2022 | 19 | 2022 |
| Adversarial generation of gene expression data R Viñas, H Andrés-Terré, P Liò, K Bryson Bioinformatics 38 (3), 730-737, 2022 | 19 | 2022 |
| Unsupervised generative and graph representation learning for modelling cell differentiation I Bica, H Andrés-Terré, A Cvejic, P Liò Scientific reports 10 (1), 9790, 2020 | 13 | 2020 |
| Is disentanglement all you need? comparing concept-based & disentanglement approaches D Kazhdan, B Dimanov, HA Terre, M Jamnik, P Liò, A Weller arXiv preprint arXiv:2104.06917, 2021 | 12 | 2021 |
| REM: An integrative rule extraction methodology for explainable data analysis in healthcare Z Shams, B Dimanov, S Kola, N Simidjievski, HA Terre, P Scherer, ... medRxiv, 2021.01. 25.21250459, 2021 | 7 | 2021 |
| GEESE: Metabolically driven latent space learning for gene expression data M Barsacchi, HA Terre, P Lió bioRxiv, 365643, 2018 | 4 | 2018 |
| Using ontology embeddings for structural inductive bias in gene expression data analysis M Trębacz, Z Shams, M Jamnik, P Scherer, N Simidjievski, HA Terre, ... arXiv preprint arXiv:2011.10998, 2020 | 3 | 2020 |
| 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 |
| 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 |
| Decoupling feature propagation from the design of graph auto-encoders P Scherer, H Andres-Terre, P Lió, M Jamnik arXiv preprint arXiv:1910.08589, 2019 | 1 | 2019 |
| Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders H Andrés-Terré, P Lió arXiv preprint arXiv:1907.05267, 2019 | 1 | 2019 |
| A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth H Coggan, H Andres Terre, P Liò Frontiers in big Data 5, 941451, 2022 | | 2022 |
| Metabolically Driven Latent Space Learning for Gene Expression Data M Barsacchi, H Andrés-Terré, P Lió Deep Learning In Biology And Medicine, 131-155, 2022 | | 2022 |
| Incorporating network based protein complex discovery into automated model construction P Scherer, M Trȩbacz, N Simidjievski, Z Shams, HA Terre, P Liò, ... arXiv preprint arXiv:2010.00387, 2020 | | 2020 |
| Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory. H Andres Terre | | 2020 |
| Structural optimization for dew-condensers using computational fluid dynamics H Andrés Terré | | 2014 |
