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| 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. Firstly, we detect subgraphs with high degrees 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 our method gains an improvement over 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 subgraph connectivity. |
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| Keywords:complexes; core-attachment; frequent pattern; protein-protein interaction (PPI) networks |
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