Classification of Protein Homo-oligomers Using Support Vector Machine

Progress in Biochemistry and Biophysics

Volumn 30, Issue 6, 2003, Pages 879-883

  Zhang, Shao Wu ^a   Pan, Quan ^a   Chen, Run Sheng ^b   Zhang, Hong Cai ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b INSTITUTE OF BIOPHYSICS   (China)

Author keywords

Bayes covariant discriminant; Classification; Homo dimer; Homo hexamer; Homo tetramer; Homo trimer; Support vector machine

Indexed keywords

DIMER; OLIGOMER; PROTEIN; TETRAMER;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; BAYES THEOREM; CLASSIFICATION; COVARIANCE; DISCRIMINANT ANALYSIS; LEARNING; MACHINE; PROTEIN ANALYSIS; PROTEIN QUATERNARY STRUCTURE;

EID: 1642534306     PISSN: 10003282     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Garian R. Prediction of quaternary structure from primary structure. Bioinformatics, 2001, 17 (6) : 551-556
2. Vapnik V. The Nature of Statistical Learning Theory. New York: Springer, 1995. 1-188
3. Vapnik V. Statistical Learning Theory. New York; Wiely, 1998. 1 -736
4. Chou K C, Elrod D W. Using discriminant function for prediction of subcellular location of prokaryotic proteins. Biochem Biophys Res Commun, 1998, 252 (1) : 63-68
5. Chou K C, Elrod D W. Protein subcellular location prediction. Protein Eng, 1999, 12 (2) : 107-108
6. Brown M, Grundy W, Lin D, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA, 2000, 97 (1) : 262-267
7. th International Conference on Intelligent systems for Molecular Biology. Menlo Park, CA; AAAI Press, 1999. 149-158
8. Zien A, Ratsch G, Mika S, et al. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics, 2000, 16 (9) : 799-807
9. Hua S J, Sun Z R. Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 2001, 17 (8) : 721-728
10. Cai Y D, Liu X J, Xu X B, et al. Support vector machines for prediction of protein subcellular location. Mol Cell Biol Res Commun, 2000, 4 (4) : 230-233
11. Cai Y D, Liu X J, Xu X B, et al. Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J Cell Biochem, 2002, 84 (2) : 343-348
12. Ding C H Q, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 2001, 17 (4) : 349-358
13. Courant R, Hubert D. Methods of mathematical physics. New York: Wildy-Interscience, 1953. 1-560
14. Joachims T. Making large-scale SVM learning practiceal. In: Scholkopf B, Burges C, Smola A, eds. Advances in Kernel Methods-support Vector Learning. Cambridge, MA: MIT Press, 1999. 1-376
15. Nakashima H, Nishikawa K, Ooi T. The folding type of a protein is relevant to the amino acid composition. J Biochem, 1986, 99 (1) : 153-62
16. Klein P. Prediction of protein structural class by discriminat analysis. Biochem Biophys Acta, 1986, 874 (2) : 205-275
17. Chou K C, Maggiora G M. Domain structure prediction. Protein Eng, 1998, 11 (7) : 523-538
18. Chou K C. A key driving force in determination of protein structural classes. Biochem Biophys Res Commun, 1999, 264 (1) : 216-224
19. Rost B, Sander C. Prediction of secondary structure at better than 70% accuracy. J Mol Biol, 1993, 232 (2) : 584-599
20. Reinhardt A, Hubbard T. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res, 1998, 26 (9) : 2230-2236
21. Emanuelsson O, Nielsen H, Brunak S, et al. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol, 2000, 300 (4) : 1005-1016