Journal of the ACM (JACM)
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
A clustering algorithm based on graph connectivity
Information Processing Letters
Average Case Analysis of Algorithms on Sequences
Average Case Analysis of Algorithms on Sequences
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Functional topology in a network of protein interactions
Bioinformatics
Modeling interactome: scale-free or geometric?
Bioinformatics
Local modeling of global interactome networks
Bioinformatics
Pairwise local alignment of protein interaction networks guided by models of evolution
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Connectedness profiles in protein networks for the analysis of gene expression data
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
On the hardness of optimization in power law graphs
COCOON'07 Proceedings of the 13th annual international conference on Computing and Combinatorics
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Computational and comparative analysis of protein-protein interaction (PPI) networks enable understanding of the modular organization of the cell through identification of functional modules and protein complexes. These analysis techniques generally rely on topological features such as connectedness, based on the premise that functionally related proteins are likely to interact densely and that these interactions follow similar evolutionary trajectories. Significant recent work in our lab, and in other labs has focused on efficient algorithms for identification of modules and their conservation. Application of these methods to a variety of networks has yielded novel biological insights. In spite of algorithmic advances, development of a comprehensive infrastructure for interaction databases is in relative infancy compared to corresponding sequence analysis tools such as BLAST and CLUSTAL. One critical component of this infrastructure is a measure of the statistical significance of a match or a dense subcomponent. Corresponding sequence-based measures such as E-values are key components of sequence matching tools. In the absence of an analytical measure, conventional methods rely on computer simulations based on ad-hoc models for quantifying significance. This paper presents the first such effort, to the best of our knowledge, aimed at analytically quantifying statistical significance of dense components and matches in reference model graphs. We consider two reference graph models – a G(n,p) model in which each pair of nodes 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 occurring in PPI networks. We argue that by choosing conservatively the value of p, the G(n,p) model will dominate that of the power-law graph that is often used to model PPI networks. We also propose a method for evaluating statistical significance based on the results derived from this analysis, and demonstrate the use of these measures for assessing significant structures in PPI networks. Experiments performed on a rich collection of PPI networks show that the proposed model provides a reliable means of evaluating statistical significance of dense patterns in these networks.