Jove
Visualize
Contact Us

Related Concept Videos

Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Electrically Tunable Piezotronic Transistor by Coupling Interface Polar Symmetry and Strain Gradient.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Identifying diagnostic markers and constructing a prognostic model for pancreatic cancer based on microarray and bioinformatic analysis.

Discover oncology·2026
Same author

Riverine inputs and sedimentary controls on phosphorus burial in the Bohai Sea: Implications for eutrophication.

Marine environmental research·2026
Same author

Acceleration of DNA reactions on the algae-based microrobots.

Journal of nanobiotechnology·2026
Same author

Metabolic reprogramming for post-acidification control in fermented milk via post-sterilization treatment.

Food microbiology·2026
Same author

In‑situ high N‑doped magnetic biochar derived from antibiotic fermentation residues for antibiotic wastewater treatment: Detoxification, adsorption performance, and mechanistic insights.

Journal of environmental management·2026
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 Experiment Video

Updated: Jun 28, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.6K

Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks.

Menglu Li, Zhiwei Wang, Luotao Liu

    IEEE Journal of Biomedical and Health Informatics
    |April 17, 2024
    PubMed
    Summary

    We introduce Subgraph-aware Graph Kernel Neural Network (SubKNet) for accurate link prediction in biological networks. SubKNet effectively captures unique node roles and shared subgraph information, outperforming existing methods.

    More Related Videos

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    706
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K

    Related Experiment Videos

    Last Updated: Jun 28, 2025

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    1.6K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    706
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K

    Area of Science:

    • Computational Biology
    • Network Science
    • Machine Learning

    Background:

    • Identifying links in biological networks is crucial for biomedical applications.
    • Existing link prediction methods often fail to account for unique node roles and shared subgraph information, limiting representation learning.
    • Subgraph-based methods can ignore valuable shared information among subgraphs.

    Purpose of the Study:

    • To develop a novel method, Subgraph-aware Graph Kernel Neural Network (SubKNet), for improved link prediction in biological networks.
    • To address limitations of existing methods by effectively learning subgraph-aware representations and distinguishing node roles.
    • To enhance the accuracy of link prediction by leveraging both subgraph structures and node embeddings.

    Main Methods:

    • SubKNet extracts a subgraph for each node pair and processes it using a graph kernel neural network.
    • Graph filters with diversity regularization are employed to decompose subgraphs for representation learning.
    • Node embeddings are utilized as auxiliary information to differentiate node pairs with similar subgraphs.

    Main Results:

    • SubKNet demonstrated superior performance compared to baseline methods on five biological networks.
    • The use of graph filters effectively distinguished node roles within different subgraphs.
    • Diversity regularization enhanced the model's capacity to learn robust and diverse representations.

    Conclusions:

    • SubKNet offers a powerful approach for link prediction in biological networks by effectively learning subgraph-aware representations.
    • The method successfully addresses the limitations of overlooking distinctive node roles and shared subgraph information.
    • SubKNet's performance highlights the importance of considering subgraph context and node roles for accurate biological network analysis.