14 citations · 20 across the 6 of their papers we have counts for
6 papers
GENIE: Generative Neural Inference for Epidemics
Laura M. Guzmán-Rincón, George R. E. Bradley, Joel Kandiah +5
The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epid…
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease
Elisa Gómez de Lope, Saurabh Deshpande, Ramón Viñas Torné +3
Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neur…
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…
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…
Factorised Neural Relational Inference for Multi-Interaction Systems
Ezra Webb, Ben Day, Helena Andres-Terre +1
Many complex natural and cultural phenomena are well modelled by systems of simple interactions between particles. A number of architectures have been developed to articulate this…