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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.
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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 polypeptide...
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

Updated: May 23, 2026

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

Published on: December 7, 2021

An algorithm for network motif discovery in biological networks.

Guimin Qin1, Lin Gao

  • 1School of Computer Science and Technology, Xidian University Xi'an, Shaanxi 710071, China. gmqin@mail.xidian.edu.cn

International Journal of Data Mining and Bioinformatics
|April 7, 2012
PubMed
Summary

This study introduces an efficient algorithm for discovering consensus motifs in biological networks. The method enhances subgraph searching and clustering to identify significant network patterns, aiding biological research.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Network motif discovery is crucial for understanding biological network organization.
  • Existing methods may face challenges with non-treelike subgraphs and consensus identification.

Purpose of the Study:

  • To develop an efficient algorithm for detecting consensus motifs in biological networks.
  • To improve the identification of statistically significant network patterns.

Main Methods:

  • Extending the Enumerate Subgraphs (ESU) algorithm for efficient subgraph searching.
  • Classifying isomorphic subgraphs and applying hierarchical clustering.
  • Deriving consensus motifs from clustered subgraphs.

Main Results:

  • The algorithm efficiently discovers motifs in Protein-Protein Interaction (PPI) and transcriptional regulatory networks.
  • Identified motifs align with existing biological knowledge.
  • Several novel consensus motifs of a given size were detected.

Conclusions:

  • The developed algorithm provides an efficient approach for consensus motif discovery in biological networks.
  • The findings can assist biologists in further exploring cellular processes and network functions.