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 networ

SUBMITTER: Rhrissorrakrai K 

PROVIDER: S-EPMC3123237 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature