Dense module enumeration in biological networks

Tsuda, K; Georgii, E

doi:10.1088/1742-6596/197/1/012012

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Dense module enumeration in biological networks

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Georgii, E
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://iopscience.iop.org/article/10.1088/1742-6596/197/1/012012/pdf
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Citation

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

Cite as: https://hdl.handle.net/21.11116/0000-0002-CB05-4

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.