MINE: Module Identification in Networks

BMC Bioinformatics

Methodology Article

Kristin C Gunsalus¹  Kahn Rhrissorrakrai¹ 

[1] Center for Genomics and Systems Biology, Department of Biology, New York University, 1009 Silver Center, 100 Washington Square East, 10003, New York, NY, USA;

关键词: Gene Ontology;    Geometric Accuracy;    Vertex Weight;    Dense Subgraph;    Composite Modularity;   

DOI  :  10.1186/1471-2105-12-192

 received in 2010-11-17, accepted in 2011-05-23,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundGraphical 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.ResultsMINE 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.ConclusionsMINE 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.

【 授权许可】

Unknown   
© BioMed Central Ltd 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.