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Extracting Biomedical Entity Relations using Biological Interaction Knowledge.

Shuyu Guo1,2, Lan Huang1,2, Gang Yao3

  • 1College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Interdisciplinary Sciences, Computational Life Sciences
|March 17, 2021
PubMed
Summary
This summary is machine-generated.

The BioGraphSAGE model effectively extracts biomedical entity relations from literature by integrating structured databases. This approach improves relation discovery, even with limited annotated data, outperforming other models.

Keywords:
Biological interaction knowledgeFew-shot learningGraph neural networksLiterature miningRelation extraction

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Bioinformatics

Background:

  • Discovering cross-type biomedical entity relations is vital for biological research.
  • Vast amounts of potential relations are embedded within biomedical literature and databases.
  • Existing methods often require extensive manual annotations, proving inefficient.

Purpose of the Study:

  • To develop a novel model for extracting biological entity relations from literature.
  • To leverage structured databases as domain knowledge for enhanced relation extraction.
  • To improve the recognition of relations between distant entities within the same text.

Main Methods:

  • Proposed the BioGraphSAGE model, a Siamese graph neural network.
  • Integrated biological semantic and positional features for relation recognition.
  • Utilized structured databases (gene, chemical, genomic, clinical) as domain knowledge.

Main Results:

  • BioGraphSAGE achieved the highest F1 score compared to other relation extraction models on small annotated datasets.
  • The model maintained a notable F1 score of 0.526 even without annotated training samples.
  • Demonstrated improved recognition of relations between distant entities.

Conclusions:

  • BioGraphSAGE offers an effective solution for biomedical relation extraction, reducing reliance on manual annotations.
  • The model's ability to incorporate domain knowledge enhances its performance, particularly in data-scarce scenarios.
  • This approach facilitates more comprehensive discovery of biological relationships from scientific literature.