33 citations · 38 across the 7 of their papers we have counts for
7 papers
Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval
Maciej Wiatrak, Eirini Arvaniti, Angus Brayne +2
A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms, an initial candidate retrieval stage that generates a shortlist…
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations
Saee Paliwal, Angus Brayne, Benedek Fabian +2
In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manif…
Phenotyping with Positive Unlabelled Learning for Genome-Wide Association Studies
Andre Vauvelle, Hamish Tomlinson, Aaron Sim +1
Identifying phenotypes plays an important role in furthering our understanding of disease biology through practical applications within healthcare and the life sciences. The challe…
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej Wiatrak, Angus Brayne +2
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed…
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Adam Foster, Árpi Vezér, Craig A Glastonbury +3
Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including…
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Daniel Neil, Joss Briody, Alix Lacoste +3
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowled…
Random Forests on Distance Matrices for Imaging Genetics Studies
Aaron Sim, Dimosthenis Tsagkrasoulis, Giovanni Montana
We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing th…