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...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...

You might also read

Related Articles

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

Sort by
Same author

Treatment patterns, clinical outcomes, and healthcare resource use associated with advanced/metastatic lung cancer in China: protocol for a retrospective observational study.

Translational lung cancer research·2021
Same author

Tumor Neovasculature-Targeted APRPG-PEG-PDLLA/MPEG-PDLLA Mixed Micelle Loading Combretastatin A-4 for Breast Cancer Therapy.

ACS biomaterials science & engineering·2021
Same author

Accelerated Bone Regeneration by MOF Modified Multifunctional Membranes through Enhancement of Osteogenic and Angiogenic Performance.

Advanced healthcare materials·2021
Same author

Robust 4d-5f Bimetal-Organic Framework for Efficient Removal of Trace SO<sub>2</sub> from SO<sub>2</sub>/CO<sub>2</sub> and SO<sub>2</sub>/CO<sub>2</sub>/N<sub>2</sub> Mixtures.

Inorganic chemistry·2021
Same author

Explore the difference between the single-chamber and dual-chamber microbial electrosynthesis for biogas production performance.

Bioelectrochemistry (Amsterdam, Netherlands)·2021
Same author

Efficient assembly of nanopore reads via highly accurate and intact error correction.

Nature communications·2021
Same journal

In silico analysis, annotation and characterisation of putative ESTs from Sorghum bicolor associated with heat stress.

International journal of bioinformatics research and applications·2015
Same journal

Docking analysis of gallic acid derivatives as HIV-1 protease inhibitors.

International journal of bioinformatics research and applications·2015
Same journal

Automatic segmentation of Potyviridae family polyproteins.

International journal of bioinformatics research and applications·2015
Same journal

Neural network and rough set hybrid scheme for prediction of missing associations.

International journal of bioinformatics research and applications·2015
Same journal

On the interconnection of stable protein complexes: inter-complex hubs and their conservation in Saccharomyces cerevisiae and Homo sapiens networks.

International journal of bioinformatics research and applications·2015
Same journal

Diversity and evolution of the envelope gene of dengue virus type 1 circulating in India in recent times.

International journal of bioinformatics research and applications·2015
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks.

Roger L Chang1, Feng Luo, Stuart Johnson

  • 1University of California San Diego, La Jolla, San Diego, CA 92093-0412, USA. rlchang@ucsd.edu

International Journal of Bioinformatics Research and Applications
|March 13, 2010
PubMed
Summary
This summary is machine-generated.

We present Deterministic Modularization of Networks (dMoNet), an improved network module identification method. dMoNet overcomes limitations of previous approaches by providing deterministic results and full network coverage for enhanced biological process analysis.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 15, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

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

Area of Science:

  • Computational Biology
  • Network Science
  • Bioinformatics

Background:

  • Network module identification is crucial for understanding biological systems.
  • The Modules of Networks (MoNet) approach uses the Girvan-Newman algorithm but suffers from systematic bias and incomplete network coverage.
  • Previous methods showed modules associated with biological processes but lacked robustness.

Purpose of the Study:

  • To develop a deterministic and comprehensive network module identification algorithm.
  • To improve upon the MoNet algorithm by addressing its limitations.
  • To enhance the analysis of biological processes through robust module detection.

Main Methods:

  • Developed a deterministic version of the Girvan-Newman algorithm.
  • Introduced a new agglomerative algorithm named Deterministic Modularization of Networks (dMoNet).
  • dMoNet processes structurally equivalent edges simultaneously for consistent results.

Main Results:

  • dMoNet generates deterministic and reproducible module identification results.
  • The algorithm achieves full network coverage, including previously excluded regions.
  • Modules identified by dMoNet are expected to show significant association with biological processes.

Conclusions:

  • dMoNet offers a robust and comprehensive solution for network module identification.
  • This method enhances the utility of network analysis for biological discovery.
  • Deterministic modularization provides a more reliable foundation for systems biology research.