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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.

David N Nicholson1, Daniel S Himmelstein1, Casey S Greene2

  • 1Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.

Biodata Mining
|October 18, 2022
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Summary
This summary is machine-generated.

Re-using label functions can accelerate biomedical knowledge graph construction. While transferring label functions across different biomedical relationship types showed mixed results, this approach can still help incorporate novel edges and discoveries into knowledge graphs.

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Area of Science:

  • Biomedical Informatics
  • Data Science

Background:

  • Biomedical knowledge graphs (KGs) are crucial for research but manual curation is slow.
  • Data programming with label functions automates data annotation but function creation is time-consuming.
  • A bottleneck exists in scaling KG population due to the effort required for label function development.

Purpose of the Study:

  • To investigate the re-usability of label functions across multiple biomedical edge types.
  • To accelerate the creation of label functions for populating biomedical knowledge graphs.

Main Methods:

  • Extracted entity-tagged abstracts focusing on compounds, genes, and diseases.
  • Trained baseline and discriminator models using label functions for relationship extraction.
  • Evaluated the impact of edge-specific and edge-mismatch label function combinations on performance.

Main Results:

  • Edge-specific label functions improved relationship extraction, while edge-mismatch functions rarely did.
  • Compound-binds-Gene and Gene-interacts-Gene relationships showed transferability.
  • The best discriminative models recalled approximately 30% of established edges in Hetionet v1.

Conclusions:

  • The framework can incorporate novel edges into existing knowledge graphs.
  • Label function transfer was effective only for highly similar edge types.
  • This strategy offers a promising approach for populating biomedical KGs with new discoveries.