most citedGCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks

14 citations · 30 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2021★ 14 cited

GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks

Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh +1

While graph neural networks (GNNs) have been shown to perform well on graph-based data from a variety of fields, they suffer from a lack of transparency and accountability, which h…

q-bio.GN2021★ 3 cited

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…

cs.LG2019★ 13 cited

Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction

Vikash Singh, Pietro Lio'

Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases. Effective solutions to the DGP problem have the potential…