A comprehensive review and evaluation of computational methods for identifying protein complexes from protein–protein interaction networks. (18th October 2019)

• Record Type: Journal Article
• Authors: Wu, Zhourun; Liao, Qing; Liu, Bin
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 5(2020)
• Page Start: 1531
• Page End: 1548
• Publication Date: 2019-10-18
• Subjects: protein complexes -- protein–protein interaction networks -- cluster-quality-based methods -- node-affinity-based methods -- ensemble clustering methods; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz085
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.