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Related Concept Videos

Protein Networks02:26

Protein Networks

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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 one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Leveraging prior knowledge for protein-protein interaction extraction with memory network.

Huiwei Zhou1, Zhuang Liu1, Shixian Ning1

  • 1School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning, China.

Database : the Journal of Biological Databases and Curation
|July 17, 2018
PubMed
Summary

This study introduces a novel memory network model for protein-protein interaction extraction from biomedical texts. This approach achieves state-of-the-art results, enhancing precision medicine applications.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Precision medicine relies on understanding protein-protein interactions (PPIs) within biological systems.
  • Automated extraction of PPIs from scientific literature is crucial for advancing biomedical research.
  • Existing methods may not fully leverage prior biological knowledge for improved PPI extraction.

Purpose of the Study:

  • To develop and evaluate a novel memory network-based model (MNM) for accurate protein-protein interaction extraction.
  • To investigate the utility of incorporating prior knowledge, including entity and relation embeddings, into the PPI extraction process.
  • To compare the performance of the proposed external memory network against traditional recurrent neural network architectures.

Main Methods:

  • A novel memory network-based model (MNM) was designed for PPI extraction.
  • The MNM integrates prior knowledge representations learned from knowledge bases, utilizing entity and relation embeddings.
  • The model employs multiple computational layers over an external memory, contrasting with local memory approaches like LSTMs.

Main Results:

  • The proposed MNM achieved state-of-the-art performance on the BioCreative VI PPI dataset.
  • Incorporating both entity and relation embeddings from prior knowledge significantly improved PPI extraction accuracy.
  • External memory networks with multiple computational layers demonstrated superior performance compared to LSTMs with local memories.

Conclusions:

  • The novel memory network-based model (MNM) effectively extracts protein-protein interactions from biomedical literature.
  • Leveraging prior knowledge through memory networks enhances the accuracy and robustness of PPI extraction models.
  • The findings support the application of advanced memory network architectures for precision medicine and biomedical knowledge discovery.