Inferring protein-protein interactions using a hybrid genetic algorithm/support vector machine method.
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.
School of Electrical & Information, Anhui University of Technology, Ma'anshan, Anhui, 243002, China. wangbing@ustc.edu
Bing
B
Wang
Peng
P
Chen
Jun
J
Zhang
Guangxin
G
Zhao
Xiang
X
Zhang
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D011485:Protein Binding / Q000235:genetics / Q000502:physiology; D017434:Protein Structure, Tertiary / Q000235:genetics / Q000502:physiology; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*
D011506:Proteins