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Identification of core–attachment complexes based on maximal frequent patterns in protein–protein interaction networks
Author(s) -
Yu Liang,
Gao Lin,
Kong ChuiLiang
Publication year - 2011
Publication title -
proteomics
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
ISBN - 978-1-4244-8304-4
DOI - 10.1002/pmic.201100194
Subject(s) - identification (biology) , core (optical fiber) , gene ontology , filter (signal processing) , computational biology , protein–protein interaction , precision and recall , biology , computer science , gene , topology (electrical circuits) , data mining , pattern recognition (psychology) , genetics , artificial intelligence , mathematics , combinatorics , telecommunications , gene expression , computer vision , botany
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.