About this Journal Submit a Manuscript Table of Contents
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.

Linked References

  1. V. Brusic, G. Rudy, M. Honeyman, J. Hammer, and L. Harrison, “Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network,” Bioinformatics, vol. 14, no. 2, pp. 121–130, 1998. View at Scopus
  2. L. Xu, L. Wencong, J. Shengli, L. Yawei, and C. Nianyi, “Support vector regression applied to materials optimization of sialon ceramics,” Chemometrics and Intelligent Laboratory Systems, vol. 82, no. 1-2, pp. 8–14, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. B. M. Nicolaï, K. I. Theron, and J. Lammertyn, “Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple,” Chemometrics and Intelligent Laboratory Systems, vol. 85, no. 2, pp. 243–252, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Qu, B.-L. Adam, Y. Yasui et al., “Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients,” Clinical Chemistry, vol. 48, no. 10, pp. 1835–1843, 2002. View at Scopus
  6. B. Niu, X.-C. Yuan, P. Roeper et al., “HIV-1 protease cleavage site prediction based on two-stage feature selection method,” Protein and Peptide Letters, vol. 20, no. 3, pp. 290–298, 2013.
  7. B. Niu, Q. Su, X.-C. Yuan, W. Lu, and J. Ding, “QSAR study on 5-lipoxygenase inhibitors based on support vector machine,” Medicinal Chemistry, vol. 8, no. 6, pp. 1108–1116, 2012.
  8. C.-R. Peng, W.-C. Lu, B. Niu, M.-J. Li, X.-Y. Yang, and M.-L. Wu, “Predicting the metabolic pathways of small molecules based on their physicochemical properties,” Protein & Peptide Letters, vol. 19, pp. 1250–1256, 2012. View at Publisher · View at Google Scholar
  9. Q. Su, W.-C. Lu, B. Niu, X. Liu, and T.-H. Gu, “Classification of the toxicity of some organic compounds to tadpoles (Rana Temporaria) through integrating multiple classifiers,” Molecular Informatics, vol. 30, no. 8, pp. 672–675, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Niu, W.-C. Lu, J. Ding et al., “Site of O-glycosylation prediction based on two stage feature selection,” Chemometrics and Intelligent Laboratory Systems, vol. 2, pp. 142–145, 2011.
  11. A. V. Finkelstein and O. B. Ptitsyn, “Why do globular proteins fit the limited set of foldin patterns?” Progress in Biophysics and Molecular Biology, vol. 50, no. 3, pp. 171–190, 1987. View at Scopus
  12. K.-C. Chou and L. Carlacci, “Energetic approach to the folding of α/β barrels,” Proteins: Structure, Function and Genetics, vol. 9, no. 4, pp. 280–295, 1991. View at Scopus
  13. K.-C. Chou, “Progress in protein structural class prediction and its impact to bioinformatics and proteomics,” Current Protein & Peptide Science, vol. 6, no. 5, pp. 423–436, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. K. Oxenoid and J. J. Chou, “The structure of phospholamban pentamer reveals a channel-like architecture in membranes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 31, pp. 10870–10875, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. J. S. Richardson, “β sheet topology and the relatedness of proteins,” Nature, vol. 268, no. 5620, pp. 495–500, 1977. View at Scopus
  16. O. B. Ptitsyn and A. V. Finkelstein, “Similarities of protein topologies: evolutionary divergence, functional convergence or principles of folding?” Quarterly Reviews of Biophysics, vol. 13, no. 3, pp. 339–386, 1980. View at Scopus
  17. B. Niu, Y.-D. Cai, W.-C. Lu, G.-Z. Li, and K.-C. Chou, “Predicting protein structural class with AdaBoost Learner,” Protein and Peptide Letters, vol. 13, no. 5, pp. 489–492, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. D. A. Doyle, J. M. Cabral, R. A. Pfuetzner et al., “The structure of the potassium channel: molecular basis of K+ conduction and selectivity,” Science, vol. 280, no. 5360, pp. 69–77, 1998. View at Publisher · View at Google Scholar · View at Scopus
  19. J. R. Schnell and J. J. Chou, “Structure and mechanism of the M2 proton channel of influenza A virus,” Nature, vol. 451, no. 7178, pp. 591–595, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Stouffer Amanda, A. Rudresh, and S. David, “Structural basis for the function and inhibition of an influenza virus proton channel,” Nature, vol. 451, pp. 596–599, 2008.
  21. M. D. Resh, “Myristylation and palmitylation of Src family members: the fats of the matter,” Cell, vol. 76, no. 3, pp. 411–413, 1994. View at Publisher · View at Google Scholar · View at Scopus
  22. K.-C. Chou and D. W. Elrod, “Protein subcellular location prediction,” Protein Engineering, vol. 12, no. 2, pp. 107–118, 1999. View at Scopus
  23. K.-C. Chou and D. W. Elrod, “Prediction of membrane protein types and subcellular locations,” Proteins, vol. 34, pp. 137–153, 1999. View at Publisher · View at Google Scholar
  24. K.-C. Chou, “A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space,” Proteins: Structure, Function and Genetics, vol. 21, no. 4, pp. 319–344, 1995. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Niu, Y.-H. Jin, K.-Y. Feng et al., “Predicting membrane protein types with bagging learner,” Protein & Peptide Letters, vol. 15, no. 6, pp. 590–594, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. V. Vapnik, Statistical Learning Theory, John Wiley & Johns, New York, NY, USA, 1998.
  27. D. L. Massart, B. G. M. Vandeginste, S. N. Deming, Y. Michotte, and L. Kaufman, Chemometrics: A Textbook, Elsevier Science Publishers B.V., Amsterdam, The Netherlands, 1988.
  28. R. Bro, “PARAFAC. Tutorial and applications,” Chemometrics and Intelligent Laboratory Systems, vol. 38, no. 2, pp. 149–171, 1997. View at Publisher · View at Google Scholar · View at Scopus
  29. W. Wu, D. L. Massart, and S. de Jong, “The kernel PCA algorithms for wide data. Part I: theory and algorithms,” Chemometrics and Intelligent Laboratory Systems, vol. 36, no. 2, pp. 165–172, 1997. View at Publisher · View at Google Scholar · View at Scopus
  30. D.-S. Cao, Y.-Z. Liang, Q.-S. Xu, Q.-N. Hu, L.-X. Zhang, and G.-H. Fu, “Exploring nonlinear relationships in chemical data using kernel-based methods,” Chemometrics and Intelligent Laboratory Systems, vol. 107, no. 1, pp. 106–115, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. G. Baudat and F. Anouar, “Generalized discriminant analysis using a kernel approach,” Neural Computation, vol. 12, no. 10, pp. 2385–2404, 2000. View at Scopus
  32. H. Yamamoto, H. Yamaji, Y. Abe et al., “Dimensionality reduction for metabolome data using PCA, PLS, OPLS, and RFDA with differential penalties to latent variables,” Chemometrics and Intelligent Laboratory Systems, vol. 98, no. 2, pp. 136–142, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Wang, Z. Hu, and Y. Zhao, “An efficient algorithm for generalized discriminant analysis using incomplete Cholesky decomposition,” Pattern Recognition Letters, vol. 28, no. 2, pp. 254–259, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. B. S. Kim and S. B. Park, “A fast k nearest neighbor finding algorithm based on the ordered partition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 761–766, 1986. View at Scopus
  35. S. Sonnenburg, G. Rätsch, S. Henschel et al., “The Shogun machine learning toolbox,” The Journal of Machine Learning Research, vol. 11, pp. 1799–1802, 2010. View at Scopus
  36. S. R. Amendolia, G. Cossu, M. L. Ganadu, B. Golosio, G. L. Masala, and G. M. Mura, “A comparative study of K-nearest neighbour, support vector machine and multi-layer perceptron for Thalassemia screening,” Chemometrics and Intelligent Laboratory Systems, vol. 69, no. 1-2, pp. 13–20, 2003. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Kearns and D. Ron, “Algorithmic stability and sanity-check bounds for leave-one-out cross-validation,” in Proceedings of the 10th Annual Conference on Computational Learning Theory, pp. 152–162, ACM Press, July 1997. View at Scopus
  38. S. B. Holden, “PAC-like upper bounds for the sample complexity of leave-one-out cross-validation,” in Proceedings of the 9th Annual Conference on Computational Learning Theory, pp. 41–50, Desenzano del Garda, Italy, July 1996. View at Scopus
  39. G.-P. Zhou and K. Doctor, “Subcellular location prediction of apoptosis proteins,” Proteins: Structure, Function and Genetics, vol. 50, no. 1, pp. 44–48, 2003. View at Publisher · View at Google Scholar · View at Scopus
  40. Y.-X. Pan, Z.-Z. Zhang, Z.-M. Guo, G.-Y. Feng, Z.-D. Huang, and L. He, “Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach,” Journal of Protein Chemistry, vol. 22, no. 4, pp. 395–402, 2003. View at Publisher · View at Google Scholar · View at Scopus
  41. K.-C. Chou and Y.-D. Cai, “Predicting protein localization in budding yeast,” Bioinformatics, vol. 21, no. 7, pp. 944–950, 2005. View at Publisher · View at Google Scholar · View at Scopus
  42. K.-C. Chou and Y.-D. Cai, “Predicting enzyme family class in a hybridization space,” Protein Science, vol. 13, no. 11, pp. 2857–2863, 2004. View at Publisher · View at Google Scholar · View at Scopus