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BioMed Research International
Volume 2013 (2013), Article ID 625403, 8 pages
http://dx.doi.org/10.1155/2013/625403
Research Article

Application of Improved Three-Dimensional Kernel Approach to Prediction of Protein Structural Class

1School of Chemistry & Chemical Engineering, Guangxi University, Guangxi Province, Nanning 530004, China
2State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
3Graduate School of the Chinese Academy of Sciences, Beijing 100049, China
4College of Pharmacy, Guangxi University of Chinese Medicine, Nanning 530001, China

Received 25 March 2013; Revised 4 May 2013; Accepted 10 May 2013

Academic Editor: Bing Niu

Copyright © 2013 Xu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Kernel methods, such as kernel PCA, kernel PLS, and support vector machines, are widely known machine learning techniques in biology, medicine, chemistry, and material science. Based on nonlinear mapping and Coulomb function, two 3D kernel approaches were improved and applied to predictions of the four protein tertiary structural classes of domains (all-α, all-β, α/β, and α + β) and five membrane protein types with satisfactory results. In a benchmark test, the performances of improved 3D kernel approach were compared with those of neural networks, support vector machines, and ensemble algorithm. Demonstration through leave-one-out cross-validation on working datasets constructed by investigators indicated that new kernel approaches outperformed other predictors. It has not escaped our notice that 3D kernel approaches may hold a high potential for improving the quality in predicting the other protein features as well. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard.