Assessing significance of connectivity and conservation in protein interaction networks.

Assessing significance of connectivity and conservation in protein interaction networks. Research Abstract Details 

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Assessing significance of connectivity and conservation in protein interaction networks. Abstract Text:

Mehmet ,Wojciech Szpankowski,Ananth Grama,

Comparative analyses of cellular interaction networks enable understanding of the cell's modular organization through identification of functional modules and complexes. These techniques often rely on topological features such as connectedness and density, based on the premise that functionally related proteins are likely to interact densely and that these interactions follow similar evolutionary trajectories. Significant recent work has focused on efficient algorithms for identification of such functional modules and their conservation. In spite of algorithmic advances, development of a comprehensive infrastructure for interaction databases is in relative infancy compared to corresponding sequence analysis tools. One critical, and as yet unresolved aspect of this infrastructure is a measure of the statistical significance of a match, or a dense subcomponent. In the absence of analytical measures, conventional methods rely on computationally expensive simulations based on ad-hoc models for quantifying significance. In this paper, we present techniques for analytically quantifying statistical significance of dense components in reference model graphs. We consider two reference models--a G(n, p) model in which each pair of nodes in a graph has an identical likelihood, p, of sharing an edge, and a two-level G(n, p) model, which accounts for high-degree hub nodes generally observed in interaction networks. Experiments performed on a rich collection of protein interaction (PPI) networks show that the proposed model provides a reliable means of evaluating statistical significance of dense patterns in these networks. We also adapt existing state-of-the-art network clustering algorithms by using our statistical significance measure as an optimization criterion. Comparison of the resulting module identification algorithm, SIDES, with existing methods shows that SIDES outperforms existing algorithms in terms of sensitivity and specificity of identified clusters with respect to available GO annotations.

Assessing significance of connectivity and conservation in protein interaction networks. Publishing Authors By Initials

M ,W Szpankowski,A Grama,

For similar biological sciences: biochemistry: proteomics research abstracts see: biological sciences: biochemistry: proteomics research

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Assessing significance of connectivity and conservation in protein interaction networks. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Journal of computational biology : a journal of co

VOLUME: 14

Page Numbers: 747-64

Journal Abbreviation: J. Comput. Biol.

ISSN: 1066-5277

DAY: 3

MONTH: 12

YEAR: 2007

Assessing significance of connectivity and conservation in protein interaction networks. Information

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LANGUAGE: eng

NlmUniqueID: 9433358

Assessing significance of connectivity and conservation in protein interaction networks. Keywords Mesh Terms:

KEYWORDS: Proteomics

MESH TERMS: metabolism

Chemical & Substance for Abstract: Assessing significance of connectivity and conservation in protein interaction networks. Information

Substance Name: Proteins

Registry Number: 0

Grant and Affiliation Information for Assessing significance of connectivity and conservation in protein interaction networks.

AFFILIATION: Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA. koyuturk@cs.purdue.edu

Country: United States

AGENCY: United States NIGMS

GRANT: R01 GM068959-01

ACRONYM: GM

MEDLINETA: J Comput Biol

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