131 citations · 136 across the 4 of their papers we have counts for
4 papers
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
Ramon Viñas Torné, Sílvia Fàbregas Salazar, Soyon Park +4
Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensional settings where large-scale i…
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…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
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…