Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices
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Abstract (arXiv)
Most phenomena related to biomedical tasks are inherently complex, and in many cases, are expressed as signals on biomedical Knowledge Graphs (KGs). In this work, we introduce the use of a new representation framework, the Prime Adjacency Matrix (PAM) for biomedical KGs, which allows for very efficient network analysis. PAM utilizes prime numbers to enable representing the whole KG with a single adjacency matrix and the fast computation of multiple properties of the network. We illustrate the applicability of the framework in the biomedical domain by working on different biomedical knowledge graphs and by providing two case studies: one on drug-repurposing for COVID-19 and one on important metapath extraction. We show that we achieve better results than the original proposed workflows, using very simple methods that require no training, in considerably less time.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 17 May 2023.
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8 of 27 references.
References · 8
- 2022Representing Long-Range Context for Graph Neural Networks with Global Attentioncited by 193
- 2020Relational Message Passing for Knowledge Graph Completioncited by 61
- 2018Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunitiescited by 43
- 2021A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspectivecited by 37
- 2021Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphscited by 25
- 2022Deep learning for drug repurposing: methods, databases, and applicationscited by 16
- 2021Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphscited by 7
- 2022Efficient multi-relational network representation using primescited by 1
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