Detecting Protein Complexes Based on Uncertain Graph Model

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 11; no. 3; pp. 486 - 497
• Main Authors: Zhao, Bihai, Wang, Jianxin, Li, Min, Wu, Fang-Xiang, Pan, Yi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2014
• Subjects: Algorithms; Bioinformatics; Clustering algorithms; Computational Biology - methods; expected density; Prediction algorithms; protein complex; Protein engineering; Protein Interaction Mapping - methods; Proteins; relative degree; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins - chemistry; Saccharomyces cerevisiae Proteins - metabolism; Sensitivity; Uncertain graph model
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2013.2297915

Advanced biological technologies are producing large-scale protein-protein interaction (PPI) data at an ever increasing pace, which enable us to identify protein complexes from PPI networks. Pair-wise protein interactions can be modeled as a graph, where vertices represent proteins and edges represent PPIs. However most of current algorithms detect protein complexes based on deterministic graphs, whose edges are either present or absent. Neighboring information is neglected in these methods. Based on the uncertain graph model, we propose the concept of expected density to assess the density degree of a subgraph, the concept of relative degree to describe the relationship between a protein and a subgraph in a PPI network. We develop an algorithm called DCU (detecting complex based on uncertain graph model) to detect complexes from PPI networks. In our method, the expected density combined with the relative degree is used to determine whether a subgraph represents a complex with high cohesion and low coupling. We apply our method and the existing competing algorithms to two yeast PPI networks. Experimental results indicate that our method performs significantly better than the state-of-the-art methods and the proposed model can provide more insights for future study in PPI networks.