Research on predicting medical entity relationships based on bipartite network representation learning

Corresponding author: WU Shengnan, vivian_sxmu@163.com

DOI: 10.12201/bmr.202407.00041

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  Abstract: Purpose/Significance To combine the research of network representation learning and link prediction for exploring their application in mining potential medical entity relationships, thereby offering a novel perspective for medical knowledge discovery. Methods/Process The literature abstracts were obtained from the PubMed database, and disease and drug treatment information within these abstracts was identified using the Subject-Action-Object (SAO) semantic mining scheme. Drug entities and disease entities were extracted, and a drug-disease bipartite network was constructed. The network structure and node characteristics were analyzed comprehensively using methods of social network analysis, network representation learning, and machine learning to uncover potential connections between medical entities. Results/Conclusion Random forest has the best effect, effectively revealing the association knowledge between drugs and diseases, and demonstrating the practical significance of the research method through the verification of the prediction results.

  Key words: Bipartite Network; SAO Semantic Mining; Network Representation Learning; Machine Learning; Medical knowledge discovery

  Submit time: 17 July 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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