Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition

Protein Engineering, Design and Selection

Volumn 17, Issue 6, 2004, Pages 509-516

  Wang, Meng ^a   Yang, Jie ^a   Liu, Guo Ping ^a   Xu, Zhi Jie ^a   Chou, Kuo Chen ^a,b,c,d  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b DONGHUA UNIVERSITY   (China)

^c Tianjin Inst Bioinfo Drug Discov   (China)

^d GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

Chou's invariance theorem; Covariant discriminant algorithm; Pseudo amino acid composition; Spectral analysis; Weighted SVM

Indexed keywords

ALGORITHMS; AMINO ACIDS; BIOLOGICAL MEMBRANES; COMPOSITION; RNA;

BIOINFORMATICS; MEMBRANE PROTEINS; WEIGHTED-SUPPORT VECTOR MACHINES;

PROTEINS;

AMINO ACID; MEMBRANE PROTEIN;

ALGORITHM; AMINO ACID COMPOSITION; ARTICLE; DATA BASE; DISCRIMINANT ANALYSIS; FOURIER TRANSFORMATION; PRIORITY JOURNAL; PROTEIN ANALYSIS; SPECTROSCOPY; TECHNOLOGY; WEIGHTED SUPPORT VECTOR MACHINE;

ALGORITHMS; AMINO ACIDS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; COMPUTING METHODOLOGIES; DATABASES, PROTEIN; MEMBRANE PROTEINS; PROTEOMICS; REPRODUCIBILITY OF RESULTS;

EID: 8444248846     PISSN: 17410126     EISSN: None     Source Type: Journal    
DOI: 10.1093/protein/gzh061     Document Type: Article

References (50)

1. Bock, J.R. and Gough, D.A. (2001) Bioinformatics, 17, 455-460.
2. Cai, Y.D. (2001) Proteins, 43, 336-338.
3. Cai, Y.D., Liu, X.J., Xu, X.B. and Chou, K.C. (2000) Mol. Cell Biol. Res. Commun., 4, 230-233.
4. Cai, Y.D., Liu, X.J., Xu, X.B. and Chou, K.C. (2002a) Comput. Chem., 26, 293-296.
5. Cai, Y.D., Liu, X.J., Xu, X.B. and Chou, K.C. (2002b) Internet Electron. J. Mol. Des., 1, 219-226.
6. Cai, Y.D., Liu, X.J., Xu, X.B. and Chou, K.C. (2002c) Peptides, 23, 205-208.
7. Cai, Y.D., Liu, X.J., Xu, X.B. and Chou, K.C. (2002d) J. Cell. Biochem., 84, 343-348.
8. Cai, Y.D., Lin, S. and Chou, K.C. (2003a) Peptides, 24, 159-161.
9. Cai, Y.D., Zhou, G.P. and Chou, K.C. (2003b) Biophys. J., 84, 3257-3263.
10. Cedano, J., Aloy, P., Perez-Pons, J.A. and Querol, E. (1997) J. Mol. Biol., 266, 594-600.
11. Chew, H.G., Crisp, D.J. and Bogner, R.E. (2000) Target detecting in radar imagery using support vector machines with training size biasing. Singapore.
12. Chew, H.G., Bogner, R.E. and Lim, C.C. (2001) Dual nu-support vector machine with error rate and training size biasing. Salt Lake City, pp. 1269-1272.
13. Chou, J.J. and Zhang, C.T. (1993) J. Theor. Biol., 161, 251-262.
14. Chou, K.C. (1988) Biophys. Chem., 30, 3-48.
15. Chou, K.C. (1989) Trends Biochem. Sci., 14, 212.
16. Chou, K.C. (1995) Proteins, 21, 319-344.
17. Chou, K.C. (1999) J. Protein Chem., 18, 473-480.
18. Chou, K.C. (2000) Biochem. Biophys. Res. Commun., 278, 477-483.
19. Chou, K.C. (2001) Proteins, 43, 246-255; Erratum, 2001, 44, 60.
20. Chou, K.C. (2002) In Weinrer, P.W. and Lu, Q. (eds), Gene Cloning and Expression Technologies. Westborough, MA, Eaton Publishing, pp. 57-70.
21. Chou, K.C. and Cai, Y.D. (2002) J. Biol. Chem., 277, 45765-45769.
22. Chou, K.C. and Cai, Y.D. (2003) J. Cell. Biochem., 90, 1250-1260; Addendum, 2004, 91, 1085.
23. Chou, K.C. and Elrod, D.W. (1999) Proteins, 34, 137-153.
24. Chou, K.C. and Zhang, C.T. (1994) J. Biol. Chem., 269, 22014-22020.
25. Chou, K.C. and Zhang, C.T. (1995) Crit. Rev. Biochem. Mol. Biol., 30, 275-349.
26. Chou, P.Y. (1980) In Abstracts of Papers, Part I, Second Chemical Congress of the North American Continent, Las Vegas.
27. Cristianini, N. and Shawe-Taylor, J. (2000) Support Vector Machines. Cambridge: Cambridge University Press.
28. Ding, C.H. and Dubchak, I. (2001) Bioinformatics, 17, 349-358.
29. Fan, X.W., Du, S.X. and Wu, T.J. (2003) J. Image Graphics, 8, 1037-1042.
30. Guo, Z.M. (2002) Master's Thesis, Shanghai Jiaotong University.
31. Hua, S.J. and Sun, Z.R. (2001) J. Mol. Biol., 308, 397-407.
32. Karush, W. (1939) MSc Thesis, University of Chicago.
33. Knerr, S., Personnaz, L. and Dreyfus, G. (eds) (1990) Single-layer Learning Revisited, a Stepwise Procedure for Building and Training Neural Networks. Berlin: Springer.
34. Lee, Y.J. and Mangasarian, O.L. (2001) RSVM, Reduced Support Vector Machines.
35. Lin, C.F. and Wang, S.D. (2002) IEEE Trans. Neural Networks, 13, 464-471.
36. Liu, W. and Chou, K.C. (1999) Protein Eng., 12, 1041-1050.
37. Mahalanobis, P.C. (1936) Proc. Natl Inst. Sci. India, 2, 49-55.
38. Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979) Multivariate Analysis. London: Academic Press, pp. 322-381.
39. Nakashima, H. and Nishikawa, K. (1994) J. Mol. Biol., 238, 54-61.
40. Nakashima, H., Nishikawa, K. and Ooi, T. (1986) J. Biochem., 99, 152-162.
41. Pan, Y.X., Zhang, Z.Z., Guo, Z.M., Feng, G.Y., Huang, Z.D. and He, L. (2003) J. Protein Chem., 22, 395-402.
42. Pillai, K.C.S. (1985) In Kotz, S. and Johnson, N.L. (eds), Encyclopedia of Statistical Sciences. New York: Wiley, pp. 176-181.
43. Schneider, G. and Wrede, P. (1994) Biophys. J., 66, 335-344.
44. Scholkopf, B., Smola, A. and Williamson, R.C. (2000) Neural Comput., 12, 1207-1245.
45. Suykens, J. and Vandewalle, J. (1999) Neural Process. Lett., 9, 293-300.
46. Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. Berlin: Springer.
47. Vapnik, V.N. (1998) Statistical Learning Theory. New York: Wiley-Interscience.
48. Zhou, G.P. (1998) J. Protein Chem., 17, 729-738.
49. Zhou, G.P. and Assa-Munt, N. (2001) Proteins, 44, 57-59.
50. Zhou, G.P. and Doctor, K. (2003) Proteins, 50, 44-48.