Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices

Konstantinos Bougiatiotis; Georgios Paliouras

Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices

Quantitative Methods 2023-05-19 v1 Machine Learning

Authors: Konstantinos Bougiatiotis , Georgios Paliouras

Abstract

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.

Keywords

biological networks and systems biology machine learning in genomics computational modeling and simulation

Cite

@article{arxiv.2305.10467,
  title  = {Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices},
  author = {Konstantinos Bougiatiotis and Georgios Paliouras},
  journal= {arXiv preprint arXiv:2305.10467},
  year   = {2023}
}

Comments

To be presented in CBMS2023. arXiv admin note: text overlap with arXiv:2209.06575

Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices

Abstract

Keywords

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