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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction.

Yang Yang1,2,3, Zixuan Zheng3, Yuyang Xu3

  • 1Computing Science and Artificial Intelligence College, Suzhou City University, No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004, China.

Briefings in Bioinformatics
|January 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces BioGSF, a novel graph-driven framework for biomedical relation extraction (RE). BioGSF enhances accuracy and efficiency in building medical knowledge graphs for healthcare AI.

Keywords:
entity-pair graphgraph neural networkrelation extractionshortest dependency paths

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Accurate biomedical relation extraction (RE) is crucial for medical knowledge graphs and healthcare AI.
  • Current methods using pre-trained language models (PLMs) often fail to fully leverage semantic and topological features.
  • There is a need for more efficient and effective RE frameworks.

Purpose of the Study:

  • To present BioGSF, a graph-driven framework for biomedical RE.
  • To improve the exploitation of semantic and topological features in RE.
  • To enhance the performance and efficiency of medical knowledge graph construction.

Main Methods:

  • Developed BioGSF, integrating shortest dependency paths (SDP) with entity-pair graphs using graph neural networks.
  • Utilized dependency relationships to derive SDPs and incorporate them into entity-pair graphs.
  • Employed graph attention networks for topological information and combined it with semantic features for relation classification.

Main Results:

  • BioGSF achieved superior performance on the S4 and BioRED datasets, with micro-F1 scores of 96.68% and 96.03%, respectively.
  • The framework demonstrated significantly shorter running times compared to previous models.
  • BioGSF effectively integrates semantic and topological information for accurate relation classification.

Conclusions:

  • BioGSF is an efficient and high-performing framework for biomedical relation extraction.
  • The graph-driven approach enhances the construction of medical knowledge graphs for healthcare AI.
  • This method offers a promising direction for advancing automated biomedical text understanding.