Helena Andres Terre
Cited by
Cited by
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
Adversarial generation of gene expression data
R Viñas, H Andrés-Terré, P Liò, K Bryson
Bioinformatics 38 (3), 730-737, 2022
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
Factorised neural relational inference for multi-interaction systems
E Webb, B Day, H Andres-Terre, P Lió
arXiv preprint arXiv:1905.08721, 2019
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
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
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
Factorised neural relational inference for multi-interaction systems. arXiv preprints
E Webb, B Day, H Andres-Terre, P Lió
arXiv preprint arXiv:1905.08721, 2019
GEESE: Metabolically driven latent space learning for gene expression data
M Barsacchi, HA Terre, P Lió
bioRxiv, 365643, 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
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
Graph representation learning on tissue-specific multi-omics
A Amor, P Lio, V Singh, RV Torné, HA Terre
arXiv preprint arXiv:2107.11856, 2021
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
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders
H Andrés-Terré, P Lió
arXiv preprint arXiv:1907.05267, 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
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
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
Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.
H Andres Terre
Structural optimization for dew-condensers using computational fluid dynamics
H Andrés Terré
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