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Understanding network concepts in modules.

Jun Dong1, Steve Horvath

  • 1Department of Human Genetics and Department of Biostatistics, University of California, Los Angeles, CA 90095, USA. jundong@ucla.edu

BMC Systems Biology
|June 6, 2007
PubMed
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We introduce approximately factorizable networks where node conformity relates to connectivity. This framework reveals simple relationships between network concepts, simplifying analysis in biology and genetics.

Area of Science:

  • Network science
  • Graph theory
  • Computational biology

Background:

  • Network concepts like clustering coefficient and connectivity are vital in biology and genetics.
  • Existing methods use topological overlap for module definition and gene annotation.

Purpose of the Study:

  • To study network concepts in approximately factorizable networks.
  • To develop a formalism relating network concepts through node conformity.
  • To demonstrate theoretical advantages of conformity-based network concepts.

Main Methods:

  • Defined node conformity as a factorizable contribution to pairwise connection strength.
  • Introduced three network concept types: fundamental, conformity-based, and approximate conformity-based.
  • Derived relationships between fundamental network concepts using the new formalism.

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Main Results:

  • Node conformity is highly related to connectivity in these networks.
  • Derived simple relationships between clustering coefficient, heterogeneity, density, centralization, and topological overlap.
  • Showed that fundamental network concepts can be approximated by connectivity functions in module networks.

Conclusions:

  • Many biological networks (protein-protein interaction, gene co-expression) are approximately factorizable.
  • Simple relationships exist between disparate network concepts in these networks.
  • Results are implemented in freely available R software.