An Efficient Approach for the Prediction of G-Protein Coupled Receptors and Their Subfamilies
G-protein coupled receptors are responsible for many physiochemical processes such as neurotransmission, metabolism, cellular growth and immune response. So it necessary to design a robust and efficient approach for the prediction of G-protein coupled receptors their subfamilies. To address the issue of efficient classification G-protein coupled receptors and their subfamilies, here in this paper we propose to use a weighted k-nearest neighbor classifier with UNION of best 50 features selected by Fisher score based feature selection, ReliefF, fast correlation based filter, minimum redundancy maximum relevancy and support vector machine based recursive feature elimination feature selection methods. The proposed method achieved an overall accuracy of 99.9, 98.3 % MCC values of 1.00, 0.98 ROC area values of 1.00, 0.998 and precision of 99.9 and 98.3 % using 10-fold cross validation to predict the G-protein coupled receptors and their subfamilies respectively.
KeywordsG-protein coupled receptors Weighted k-nearest neighbor Minimum redundancy maximum relevance Sequence derived properties Matthew’s correlation coefficient
- 1.Bhasin, M., Raghava, G.P.S.: GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Res. 32(Suppl. 2), W383–W389 (2004)Google Scholar
- 4.Gu, Q., Ding, Y. Binary particle swarm optimization based prediction of G-protein-coupled receptor families with feature selection. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 171–176. ACM (June 2009)Google Scholar
- 8.Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new algorithm. In: AAAI, pp. 129–134 (July 1992)Google Scholar
- 9.Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: ICML, vol. 3, pp. 856–863 (August 2003)Google Scholar
- 11.Yu, Y. SVM-RFE Algorithm for Gene Feature Selection. Computer Engineering (2008)Google Scholar