Functional topology in a network of protein interactions
Bioinformatics
Multilevel algorithms for partitioning power-law graphs
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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
Dividing Protein Interaction Networks by Growing Orthologous Articulations
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Dividing protein interaction networks for modular network comparative analysis
Pattern Recognition Letters
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Advances in modern technologies for measuring protein-protein interaction (PPI) has boosted research in PPI networks analysis and comparison. One of the challenging problems in comparative analysis of PPI networks is the comparison of networks across species for discovering conserved modules. Approaches for this task generally merge the considered networks into one new weighted graph, called alignment graph, which describes how interaction between each pair of proteins is preserved in different networks. The problem of finding conserved protein complexes across species is then transformed into the problem of searching the alignment graph for subnetworks whose weights satisfy a given constraint. Because the latter problem is computationally intractable, generally greedy techniques are used. In this paper we propose an alternative approach for this task. First, we use a technique we recently introduced for dividing PPI networks into small subnets which are likely to contain conserved modules. Next, we perform network alignment on pairs of resulting subnets from different species, and apply an exact search algorithm iteratively on each alignment graph, each time changing the constraint based on the weight of the solution found in the previous iteration. Results of experiments show that this method discovers multiple accurate conserved modules, and can be used for refining state-of-the-art algorithms for comparative network analysis.