title: MINE: Module Identification in Networks.
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.
authors: Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA.
authors: Kahn
authors: K
authors: Rhrissorrakrai
authors: Kristin C
authors: KC
authors: Gunsalus
references: J Mol Biol. 2001 Dec 14;314(5):1041-52
references: 11743721
references: Nature. 1998 Jun 4;393(6684):440-2
references: 9623998
references: Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9
references: 16381927
references: Bioinformatics. 2010 Apr 15;26(8):1105-11
references: 20185405
references: Bioinformatics. 2006 Apr 15;22(8):1021-3
references: 16473872
references: Nat Genet. 2000 May;25(1):25-9
references: 10802651
references: Cell. 2008 Aug 8;134(3):534-45
references: 18692475
references: Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31
references: 19850723
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jun;79(6 Pt 2):066107
references: 19658564
references: Nature. 2004 Jul 1;430(6995):88-93
references: 15190252
references: Nucleic Acids Res. 2002 Apr 1;30(7):1575-84
references: 11917018
references: Nucleic Acids Res. 2002 Jan 1;30(1):31-4
references: 11752246
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113
references: 14995526
references: Bioinformatics. 2007 Jul 1;23(13):i577-86
references: 17646346
references: Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9
references: 19897547
references: Bioinformatics. 2005 Jun;21 Suppl 1:i213-21
references: 15961460
references: Nat Methods. 2009 Jan;6(1):47-54
references: 19123269
references: BMC Bioinformatics. 2003 Jan 13;4:2
references: 12525261