IdenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Wu, Zhourun; Liao, Qing; Liu, Bin
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 1972
• Page End: 1983
• Publication Date: 2020-02-17
• Subjects: protein complexes -- protein–protein interaction networks -- mutual important interacting partner relation; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa016
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein–protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.