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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Protein-protein interaction relation extraction based on multigranularity semantic fusion.

Yijing Li1, Yanping Chen1, Yongbin Qin1

  • 1College of Computer Science and Technology, Guizhou University, Guiyang, China.

Journal of Biomedical Informatics
|October 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multigranularity semantic fusion method to improve biomedical relation extraction. The approach enhances protein-protein interaction (PPI) extraction by combining global and local semantic information, achieving high accuracy on standard datasets.

Keywords:
Biological information extractionMultigranularity semantic fusionRelation extraction

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Extracting semantic relationships between biomedical entities is crucial for information extraction.
  • Existing methods often focus on global sentence semantics, potentially overlooking local word-level nuances.
  • Language semantics are expressed across multiple granularities, influenced by local context.

Purpose of the Study:

  • To propose a novel multigranularity semantic fusion method for biomedical relation extraction.
  • To enhance the discriminability of neural networks by integrating global and local semantic representations.
  • To improve the accuracy of protein-protein interaction (PPI) extraction.

Main Methods:

  • Utilized the Transformer model for embedding sentence words into distributed representations to capture global semantics.
  • Applied a multichannel strategy to encode local semantics, allowing words to have varied representations within a sentence.
  • Fused both global and local semantic representations for enhanced neural network performance.

Main Results:

  • Achieved high F1-scores on five standard PPI corpora: AImed (83.4%), BioInfer (89.9%), IEPA (81.2%), HPRD50 (84.5%), and LLL (92.5%).
  • Demonstrated the effectiveness of the multigranularity semantic fusion approach.
  • Showcased significant improvements in protein-protein interaction relationship extraction.

Conclusions:

  • Multigranularity semantic fusion is beneficial for biomedical relation extraction.
  • The proposed method effectively integrates global and local semantic information.
  • This approach offers a promising direction for advancing biomedical information extraction tasks, particularly PPI extraction.