MINE: Module Identification in NEtworks

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 MIN...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Kahn Rhrissorrakrai, Kristin C. Gunsalus
• Keywords: Biometrics, BMCBI 2011, C Elegans, Protein Interaction, Related Gene Products