This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)
Research
Functional characterization and topological modularity of molecular interaction networks
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* Corresponding author: Jayesh Pandey jpandey@cs.purdue.edu
1 Department of Computer Science, Purdue University, West Lafayette, IN, USA
2 Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH, USA
3 Center for Proteomics & Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
BMC Bioinformatics 2010, 11(Suppl 1):S35 doi:10.1186/1471-2105-11-S1-S35
Published: 18 January 2010Abstract
Background
Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets) occurring in close proximity to infer functional associations.
Results
In this paper we perform a formal comparative investigation of the relationship between functional coherence and topological proximity in networks. We investigate the problem of assessing the coherence of sets of biomolecules (or segments thereof) taking into account functional specificity as well as the distribution of functional attributes across entity groups. We also propose novel measures of topological proximity that are more robust to noisy and incomplete interaction data.
Conclusion
We derive the following results in this paper: (i) there exists strong correlation between functional similarity and topological proximity in various network abstractions, with domain interaction networks (DDIs) demonstrating higher correlation than protein interaction networks (PPIs); (ii) measures that quantify coherence among entire sets of proteins are superior to aggregates of known pair-wise measures; and (iii) random-walk based measures of topological proximity are better suited to existing interaction data. We validate our methods on diverse data, including experimentally and computationally derived PPIs and DDIs, as well as on sets of known biologically related groups of molecules.