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Protein-protein interaction extraction enhanced by entity semantic representation.

Xinyu He1, Binhe Li1, Xiaolu Xu1

  • 1School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, China.

Health Information Science and Systems
|January 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced protein-protein interaction (PPI) extraction model using enhanced entity semantics. The novel approach overcomes data limitations and improves accuracy in biomedical text mining.

Keywords:
Entity semantic enhancementLarge language modelProtein–protein interaction extraction

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Area of Science:

  • Biomedical text mining
  • Bioinformatics
  • Natural Language Processing

Background:

  • High costs and data scarcity challenge high-quality protein-protein interaction (PPI) corpus construction.
  • Diverse semantic expressions in biomedical texts complicate accurate PPI extraction.
  • Existing methods struggle with comprehensive semantic understanding for PPI identification.

Purpose of the Study:

  • To develop an enhanced protein-protein interaction (PPI) extraction model leveraging entity semantics.
  • To address challenges in biomedical text mining, including data scarcity and semantic diversity.
  • To improve the accuracy and efficiency of identifying protein interactions from text.

Main Methods:

  • Developed an Attention-based Contextual Information Enhancement module to capture relation-relevant semantics.
  • Implemented a large language model-based Multi-dimensional Semantic Information Enhancement module for rich entity representations.
  • Designed a Multimodal Language-Interaction Protein Graph Encoder to fuse textual semantics and structural information for relation prediction.

Main Results:

  • The proposed model significantly outperforms existing techniques on five standard PPI datasets (AIMed, BioInfer, HPRD50, IEPA, LLL).
  • Achieved optimal performance in average F1-score, demonstrating superior accuracy in PPI extraction.
  • Ablation experiments validated the effectiveness of individual modules and their synergistic contribution.

Conclusions:

  • The research presents a breakthrough in protein-protein interaction extraction.
  • The proposed model offers novel technical approaches and insights for biomedical text mining.
  • This work enhances the capability of extracting complex biological relationships from unstructured text data.