MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm. (October 2015)

• Record Type: Journal Article
• Authors: Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Song, Dan
• Is Part Of: Computational biology and chemistry. Volume 58(2015)
• Page Start: 173
• Page End: 181
• Publication Date: 2015-10
• Subjects: Protein–protein interaction (PPI) network -- Protein complex -- Multiobjective evolutionary -- Normalized clustering score; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.06.006
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A method of multiobjective problem based on network topological property is proposed. A method of identifying protein complexes based on MOEPGA in PPI network is proposed. Results show MOEPGA is superior to several prediction algorithms. Abstract: The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore . Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.