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Pattern Mining Across Many Massive Biological Networks

  • Wenyuan Li
  • Haiyan Hu
  • Yu Huang
  • Haifeng Li
  • Michael R. Mehan
  • Juan Nunez-Iglesias
  • Min Xu
  • Xifeng Yan
  • Xianghong Jasmine Zhou
Chapter

Abstract

The rapid accumulation of biological network data is creating an urgent need for computational methods on integrative network analysis. Thus far, most such methods focused on the analysis of single biological networks. This chapter discusses a suite of methods we developed to mine patterns across many biological networks. Such patterns include frequent dense subgraphs, frequent dense vertex sets, generic frequent patterns, and differential subgraph patterns. Using the identified network patterns, we systematically perform gene functional annotation, regulatory network reconstruction, and genome to phenome mapping. Finally, tensor computation of multiple weighted biological networks, which filled a gap of integrative network biology, is discussed.

Notes

Acknowledgements

The work presented in this chapter was supported by National Institutes of Health Grants R01GM074163, P50HG002790, and U54CA112952 and NSF Grants 0515936, 0747475 and DMS-0705312.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Wenyuan Li
    • 1
  • Haiyan Hu
    • 2
  • Yu Huang
    • 1
  • Haifeng Li
    • 3
  • Michael R. Mehan
    • 1
  • Juan Nunez-Iglesias
    • 1
  • Min Xu
    • 1
  • Xifeng Yan
    • 4
  • Xianghong Jasmine Zhou
    • 5
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.University of Central FloridaOrlandoUSA
  3. 3.Motorola LabsLos AngelesUSA
  4. 4.University of CaliforniaSanta BarbaraUSA
  5. 5.Program in Computational Biology, Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUSA

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