14 citations · 21 across the 7 of their papers we have counts for
7 papers
Graph Representation Learning on Tissue-Specific Multi-Omics
Amine Amor, Pietro Lio', Vikash Singh +2
Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embed…
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Dmitry Kazhdan, Botty Dimanov, Helena Andres Terre +3
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement le…
Using ontology embeddings for structural inductive bias in gene expression data analysis
Maja Trębacz, Zohreh Shams, Mateja Jamnik +4
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. However, such data is extremely highly dimen…
Incorporating network based protein complex discovery into automated model construction
Paul Scherer, Maja Trȩbacz, Nikola Simidjievski +4
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs. The s…
Decoupling feature propagation from the design of graph auto-encoders
Paul Scherer, Helena Andres-Terre, Pietro Lio +1
We present two instances, L-GAE and L-VGAE, of the variational graph auto-encoding family (VGAE) based on separating feature propagation operations from graph convolution layers ty…
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders
Helena Andrés-Terré, Pietro Lió
The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years. They have been developed as denoising, clustering and generativ…