MINE: Module Identification in Networks

Kahn Rhrissorrakrai, Kristin C. Gunsalus

Research output: Contribution to journalArticle

Abstract

Background: Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks.Results: MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.Conclusions: MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans.

Original languageEnglish (US)
Article number192
JournalBMC Bioinformatics
Volume12
DOIs
StatePublished - May 23 2011

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Identification (control systems)
Genes
Proteins
Protein Interaction Maps
Module
Modularity
Caenorhabditis elegans Proteins
Protein Interaction Networks
Biological Networks
Cluster Analysis
Protein-protein Interaction
Gene
Molecular interactions
Molecular Sequence Annotation
Clustering algorithms
Gene Regulatory Networks
Data Association
Customization
Saccharomyces cerevisiae
Saccharomyces Cerevisiae

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics
  • Structural Biology

Cite this

MINE : Module Identification in Networks. / Rhrissorrakrai, Kahn; Gunsalus, Kristin C.

In: BMC Bioinformatics, Vol. 12, 192, 23.05.2011.

Research output: Contribution to journalArticle

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