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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.

Ai-Ru Hsieh1, Chen-Yu Tsai2

  • 1Department of Statistics, Tamkang University, Tamsui District, New Taipei City, 251301, Taiwan. airudropbox@gmail.com.

European Journal of Medical Research
|August 2, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distant supervision method for automatically extracting gene-gene interactions, reducing manual annotation costs. This approach aids in understanding complex disease heritability and advancing precision medicine.

Keywords:
Biomedical text miningGene–gene interactionGraph kernel

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Area of Science:

  • Biomedical informatics
  • Machine learning
  • Genomics

Background:

  • Supervised machine learning for biomedical relationship extraction demands extensive manual annotation.
  • Existing methods lack focus on gene-gene interactions, crucial for understanding complex diseases.
  • Distant supervision offers a practical alternative by leveraging knowledge bases for automatic annotation.

Purpose of the Study:

  • To develop and evaluate an automated method for extracting gene-gene interactions.
  • To address the gap in biomedical relationship extraction studies focusing on gene-gene interactions.
  • To facilitate the understanding of human complex disease heritability.

Main Methods:

  • Utilized the KEGG PATHWAY database for gene-gene interaction information.
  • Generated a training dataset from PubMed abstracts.
  • Employed a graph kernel method for relationship extraction.
  • Implemented a distant supervision approach to eliminate manual labeling.

Main Results:

  • Achieved a high F1-score of 0.79 for gene-gene interaction extraction.
  • Successfully demonstrated the effectiveness of the distant supervision method.
  • Validated the applicability of graph kernel methods in this domain.

Conclusions:

  • The developed distant supervision method significantly reduces time and cost associated with manual data annotation.
  • The graph kernel-based approach is effective for extracting gene-gene interactions.
  • This work is expected to contribute to the advancement of precision medicine.