Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Elevated platelet-to-lymphocyte ratio is associated with increased breast cancer risk: A cross-sectional analysis of the NHANES 2009 to 2020.

Medicine·2026
Same author

Investigating the human anellome across the lifespan reveals sex-specific biphasic trajectories.

npj aging·2026
Same author

Plasma proteomic profiling uncovers the age-related molecular changes in chronic obstructive pulmonary disease.

BMC pulmonary medicine·2026
Same author

Integrated Bulk and Single-Cell Transcriptomic Analysis Reveals Xenobiotic Metabolism Genes Drive Progression From Liver Cirrhosis to Hepatocellular Carcinoma.

Human mutation·2026
Same author

Molecular Transformation and Climate Effects of Water-Soluble Brown Carbon from Urban on-Road Vehicular Emissions.

Environmental science & technology·2026
Same author

Proteomics-decoded capture kill mechanism of CuAgOx/UiO-66-NH<sub>2</sub> for dark inactivation of pathogenic bacteria.

Journal of hazardous materials·2026

Related Experiment Video

Updated: May 29, 2026

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

Neighborhood hash graph kernel for protein-protein interaction extraction.

Yijia Zhang1, Hongfei Lin, Zhihao Yang

  • 1School of Electronics and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China. zhyj@dlut.edu.cn

Journal of Biomedical Informatics
|September 3, 2011
PubMed
Summary
This summary is machine-generated.

We developed a new method using neighborhood hash graph kernels for automated protein-protein interaction (PPI) extraction from biomedical texts. This approach efficiently processes dependency graphs, achieving state-of-the-art results faster than existing methods.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: May 29, 2026

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

  • Biomedical text mining
  • Bioinformatics
  • Natural Language Processing

Background:

  • Automated extraction of protein-protein interactions (PPIs) is crucial for understanding biological pathways.
  • Existing graph kernel methods for PPI extraction can be computationally intensive.
  • Representing sentence structure effectively is key for accurate interaction identification.

Purpose of the Study:

  • To propose a novel approach for automated PPI extraction using neighborhood hash graph kernels.
  • To improve the efficiency and accuracy of PPI extraction from biomedical literature.
  • To leverage full dependency graphs while controlling computational complexity.

Main Methods:

  • Developed a neighborhood hash graph kernel approach for PPI extraction.
  • Utilized full dependency graphs to represent sentence structures.
  • Evaluated the approach on five publicly available PPI corpora.

Main Results:

  • The proposed approach demonstrated performance comparable to state-of-the-art PPI extraction systems.
  • The method was significantly faster than the all-path graph kernel approach across all tested corpora.
  • Effective utilization of dependency graphs was achieved with controlled computational cost.

Conclusions:

  • The neighborhood hash graph kernel approach offers an efficient and effective solution for automated PPI extraction.
  • This method provides a viable alternative to existing graph kernel techniques, balancing performance and speed.
  • The findings contribute to advancing biomedical text mining for biological discovery.