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Reference: Yu L, et al. (2011) Identification of core-attachment complexes based on maximal frequent patterns in protein-protein interaction networks. Proteomics 11(19):3826-34

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Abstract


In this paper, we present a method for core-attachment complexes identification based on maximal frequent patterns (CCiMFP) in yeast protein-protein interaction (PPI) networks. First, we detect subgraphs with high degree as candidate protein cores by mining maximal frequent patterns. Then using topological and functional similarities, we combine highly similar protein cores and filter insignificant ones. Finally, the core-attachment complexes are formed by adding attachment proteins to each significant core. We experimentally evaluate the performance of our method CCiMFP on yeast PPI networks. Using gold standard sets of protein complexes, Gene Ontology (GO), and localization annotations, we show that our method gains an improvement over the previous algorithms in terms of precision, recall, and biological significance of the predicted complexes. The colocalization scores of our predicted complex sets are higher than those of two known complex sets. Moreover, our method can detect GO-enriched complexes with disconnected cores compared with other methods based on the subgraph connectivity.

Reference Type
Journal Article
Authors
Yu L, Gao L, Kong C
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Interaction Annotations


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Interactor Interactor Type Assay Annotation Action Modification Phenotype Source Reference

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Gene Gene Ontology Term Qualifier Aspect Method Evidence Source Assigned On Annotation Extension Reference

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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

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