Using AdaboostSVM to Predict the GPCR Functional Classes

AdaBoost incorporating properly designed RBFSVM is a popular boosting method and demonstrates better generalization performance than SVM on imbalanced classification problems. This paper discusses the application of AdaBoostSVM algorithms to the problem of G-protein-coupled receptor classes prediction in which the pseudo amino acid composition is derived by combining "the cellular automaton image" and the Ziv-Lempel complexity. In the experiments of classifing GPCRs form no-GPCRs and GPCRs\´ six main families, the jackknife test overall accuracy rates are 96.8% and 89.04%, respectively. The experimental results suggest that the AdaBoostSVM holds potential to be a useful algorithm for understanding the functions of GPCRs and other proteins.