Protein complex prediction via cost-based clustering
Bioinformatics
Noise-induced cooperative behavior in a multicell system
Bioinformatics
Alignment of metabolic pathways
Bioinformatics
Bioinformatics
BIBE '09 Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering
Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
A hybrid clustering algorithm for identifying modules in Protein Protein Interaction networks
International Journal of Data Mining and 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
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Recently, accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules (e.g., protein complexes) groups of vertices within which connections are dense while between which they are sparse. These functional modules always correspond to well-known protein complexes, which may be evolutionarily conserved across multiple species. Therefore, in this paper, we propose a method based on module alignment, which integrates protein interaction, and sequence information for finding conserved protein complexes. First, our method decomposes Protein-Protein Interaction (PPI) networks into modules by module detection methods, and then identifies conserved complexes by module alignment based on sequence similarity between pairs of proteins from each of the species. We test our method between Saccharomyces cerevisiae and Drosophila melanogaster. The results show that our method gets a higher accuracy for identification of conserved complexes.