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Related Concept Videos

Symmetry01:26

Symmetry

The equation of an ellipse centered at the origin defines all points whose distances from the center maintain a constant ratio between the horizontal and vertical axes. This equation results in a smooth, closed curve that extends further along the x-axis than the y-axis, giving it a horizontal orientation. Such an ellipse demonstrates three kinds of symmetry: across the x-axis, across the y-axis, and about the origin. These symmetries are essential in understanding the graph's structure and...
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Once the fields have been calculated using Maxwell's four equations, the Lorentz force equation gives the force that the fields exert on a charged particle moving with a certain velocity. The Lorentz force equation combines the force of the electric field and of the magnetic field on the moving charge. Maxwell's equations and the Lorentz force law together encompass all the laws of electricity and magnetism. The symmetry that Maxwell introduced into his mathematical framework may not be...
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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

Symmetry compression method for discovering network motifs.

Jianxin Wang1, Yuannan Huang, Fang-Xiang Wu

  • 1School of Information Engineering and Science, Central South University, Computer Building, Changsha, Hunan, P.R. China. jxwang@mail.csu.edu.cn

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Symmetry Compression for Motif Detection (SCMD), a novel method to efficiently discover network motifs in biological systems. SCMD reduces redundant calculations in symmetric networks, enabling faster and more comprehensive motif discovery.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Analysis

Background:

  • Biological networks exhibit significant symmetry (automorphism).
  • This inherent symmetry leads to redundant calculations in network motif discovery.
  • Existing methods struggle with computational efficiency on highly symmetric networks.

Purpose of the Study:

  • To develop an efficient method for network motif discovery that addresses the challenge of network symmetry.
  • To reduce redundant computations caused by basic symmetric subgraphs (BSSs).
  • To improve the performance of existing exact and sampling algorithms for motif detection.

Main Methods:

  • Proposed Symmetry Compression method for Motif Detection (SCMD).
  • Compression of basic symmetric subgraphs (BSSs) before motif extraction.
  • Development of an efficient lossless decompression algorithm for compressed subgraphs.
  • Integration of SCMD with established exact and sampling motif detection algorithms.

Main Results:

  • SCMD is a lossless method, yielding identical results to original algorithms for exact motif detection.
  • SCMD minimally impacts the quality of sampling-based motif detection results.
  • Significant speedups were observed when applying SCMD to highly symmetric biological networks.
  • SCMD facilitates the discovery of larger network motifs in symmetric biological networks.

Conclusions:

  • SCMD effectively eliminates redundant calculations in symmetric biological networks.
  • The method enhances the efficiency of both exact and sampling motif detection algorithms.
  • SCMD expands the capability to identify larger and more complex network motifs in systems biology.