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  Dense module enumeration in biological networks

Tsuda, K., & Georgii, E. (2009). Dense module enumeration in biological networks. Bristol, UK: Institute of Physics. doi:10.1088/1742-6596/197/1/012012.

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Genre: Conference Paper

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 Creators:
Tsuda, K, Author              
Georgii, E1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

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 Dates: 2009-12
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1088/1742-6596/197/1/012012
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Title: International Workshop on Statistical-Mechanical Informatics (IW-SMI 2009)
Place of Event: Kyoto, Japan
Start-/End Date: 2009-09-13 - 2009-09-16

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Title: Journal of Physics: Conference Series
Source Genre: Series
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Publ. Info: Bristol, UK : Institute of Physics
Pages: - Volume / Issue: 197 (1) Sequence Number: 012012 Start / End Page: 1 - 10 Identifier: -