Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms

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

Research output: Contribution to journalReview ArticleResearchpeer-review

4 Citations (Scopus)


The rapid accumulation of biological network data is creating an urgent need for computational methods capable of integrative network analysis. This paper discusses a suite of algorithms that we have developed to discover biologically significant patterns that appear frequently in multiple biological networks: coherent dense subgraphs, frequent dense vertex-sets, generic frequent subgraphs, differential subgraphs, and recurrent heavy subgraphs. We demonstrate these methods on gene co-expression networks, using the identified patterns to systematically annotate gene functions, map genome to phenome, and perform high-order cooperativity analysis.

Original languageEnglish
Pages (from-to)157-176
Number of pages20
JournalStatistics in BioSciences
Issue number1
Publication statusPublished - 1 May 2012
Externally publishedYes


  • Coherent dense subgraph
  • Differential subgraph
  • Frequent dense vertex-set
  • Frequent pattern
  • Generic frequent subgraph
  • Integrative network analysis
  • Recurrent heavy subgraph
  • Tensor representation of multiple networks

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