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Updated: Jul 27, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Multimodal learning on graphs for disease relation extraction.

Yucong Lin1, Keming Lu2, Sheng Yu3

  • 1Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China; Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.

Journal of Biomedical Informatics
|June 5, 2023
PubMed
Summary
This summary is machine-generated.

REMAP, a multimodal approach, enhances disease relation extraction by fusing incomplete knowledge graphs with medical text. This method improves accuracy and F1-scores, enabling better discovery of disease connections.

Keywords:
Disease relation extractionGraph neural networksKnowledge graphsLanguage modelsMedical relation extractionMultimodal learning

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Area of Science:

  • Artificial Intelligence
  • Biomedical Informatics
  • Natural Language Processing

Background:

  • Disease knowledge graphs are vital for organizing complex disease information.
  • Extracting accurate disease relations is challenging due to scattered data in text and incomplete graphs.
  • Multimodal data fusion is needed for comprehensive knowledge graph construction.

Purpose of the Study:

  • To introduce REMAP, a novel multimodal approach for disease relation extraction.
  • To improve the accuracy and completeness of disease knowledge graphs by integrating diverse data sources.
  • To enable robust relation extraction even with missing data modalities.

Main Methods:

  • REMAP jointly embeds incomplete knowledge graphs and medical text datasets into a latent vector space.
  • A decoupled model structure allows for single-modal inference, addressing missing data scenarios.
  • The approach was applied to a large-scale disease knowledge graph and text corpus.

Main Results:

  • REMAP improved language-based disease relation extraction by 10.0% in accuracy and 17.2% in F1-score.
  • The method outperformed graph-based approaches by 8.4% in accuracy and 10.4% in F1-score for recommending new relationships.
  • Fusion of knowledge graphs and language data significantly enhanced relation extraction.

Conclusions:

  • REMAP offers a flexible and effective multimodal strategy for disease relation extraction.
  • The approach successfully integrates structured knowledge with unstructured text data.
  • This model facilitates the discovery, access, and evaluation of disease concept relationships.