Inferring Protein-Protein Interactions Using a Hybrid Genetic Algorithm/Support Vector Machine Method

Title: Inferring Protein-Protein Interactions Using a Hybrid Genetic Algorithm/Support Vector Machine Method

Volume: 17 Issue: 9

Author(s): Bing Wang, Peng Chen, Jun Zhang, Guangxin Zhao and Xiang Zhang

Affiliation:

Keywords: Protein-protein interaction, protein-domain relations, genetic algorithm, support vector machine, domain composition, composition transformation

Abstract: Identifying protein-protein interaction is crucial for understanding the biological systems and processes, as well as mutant design. This paper proposes a novel hybrid Genetic Algorithm/Support Vector Machine (GA/SVM) method to predict the interactions between proteins intermediated by the protein-domain relations. A protein domain is a structural and/or functional unit of the protein. Every protein can be characterized by a distinct domain or a sequential combination of multiple domains. In our method, the protein was first represented by its domains where the effects of domain duplication were also considered. Transformation of the domain composition was taken to simulate the combination of different domains using genetic algorithm (GA). The optimal transformation was discovered using a predictor constructed by a support vector machines (SVM) method. Compared with random predictor, the prediction performance of our method is more effective and efficient with 0.85 sensitivity, 0.90 specificity and 0.88 accuracy.

Cite this article as:

Wang Bing, Chen Peng, Zhang Jun, Zhao Guangxin and Zhang Xiang, Inferring Protein-Protein Interactions Using a Hybrid Genetic Algorithm/Support Vector Machine Method, Protein & Peptide Letters 2010; 17 (9) . https://dx.doi.org/10.2174/092986610791760379

DOI https://dx.doi.org/10.2174/092986610791760379	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305