activity
20172021
most citedCausal Embeddings for Recommendation

233 citations · 821 across the 23 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.LG2021

A Unified View of Relational Deep Learning for Drug Pair Scoring

Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov +3

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design ta…

cs.LG2021

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Stephen Bonner, Ufuk Kirik, Ola Engkvist +2

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize the interconnected nature of the domain…

q-bio.QM2021★ 14 cited

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets

Cheng Ye, Rowan Swiers, Stephen Bonner +1

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attritio…

q-bio.BM2021★ 66 cited

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

Stephen Bonner, Ian P Barrett, Cheng Ye +4

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in…

cs.AI2021★ 143 cited

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Stephen Bonner, Ian P Barrett, Cheng Ye +5

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of…