# Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices

arXiv: https://arxiv.org/abs/2305.10467 · HTML: https://mlsanity.com/paper/2305.10467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 8 of 8)

- [Representing Long-Range Context for Graph Neural Networks with Global Attention](https://mlsanity.com/paper/2201.08821/) (2022)
- [Relational Message Passing for Knowledge Graph Completion](https://mlsanity.com/paper/2002.06757/) (2020)
- [Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities](https://mlsanity.com/paper/1807.00123/) (2018)
- [A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective](https://mlsanity.com/paper/2102.10062/) (2021)
- [Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2103.10367/) (2021)
- [Deep learning for drug repurposing: methods, databases, and applications](https://mlsanity.com/paper/2202.05145/) (2022)
- [Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2111.10625/) (2021)
- [Efficient multi-relational network representation using primes](https://mlsanity.com/paper/2209.06575/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis](https://mlsanity.com/paper/2411.11149/) (2024)

## 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.