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Knowledge-aware attention network for protein-protein interaction extraction.

Huiwei Zhou1, Zhuang Liu1, Shixian Ning1

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.

Journal of Biomedical Informatics
|June 17, 2019
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Summary
This summary is machine-generated.

This study introduces a knowledge-aware attention network (KAN) for extracting protein-protein interactions (PPIs) from literature. The KAN model effectively integrates prior knowledge from knowledge bases, improving precision medicine research.

Keywords:
Attention mechanismPPI extractionPrior knowledge

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Protein-protein interaction (PPI) extraction from scientific literature is crucial for advancing precision medicine.
  • Current PPI extraction methods often require extensive feature engineering and underutilize knowledge bases (KBs).
  • KBs offer structured information vital for enhancing PPI extraction accuracy.

Purpose of the Study:

  • To propose a novel knowledge-aware attention network (KAN) for improved PPI extraction.
  • To effectively fuse prior knowledge from KBs with contextual information for PPI identification.
  • To overcome limitations of existing methods in feature engineering and knowledge integration.

Main Methods:

  • Developed a knowledge-aware attention network (KAN) model.
  • Employed a diagonal-disabled multi-head attention mechanism to encode context and KB-derived knowledge representations.
  • Utilized a multi-dimensional attention mechanism for optimal feature selection from encoded context.

Main Results:

  • The KAN model successfully integrated prior knowledge from KBs into the PPI extraction process.
  • Demonstrated the ability to capture knowledge-aware dependencies between words within a sequence.
  • Achieved state-of-the-art performance on the BioCreative VI PPI dataset.

Conclusions:

  • The proposed KAN model offers a significant advancement in automated PPI extraction.
  • Integrating knowledge from KBs via attention mechanisms enhances the accuracy and efficiency of PPI identification.
  • This approach holds promise for accelerating precision medicine through better utilization of scientific literature.