Predicting protein structural class by SVM with class-wise optimized features and decision probabilities

Journal of Theoretical Biology

Volumn 253, Issue 2, 2008, Pages 375-380

  Anand, Ashish ^a   Pugalenthi, Ganesan ^a   Suganthan, P N ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Multi class SVM; Probability outputs SVM; SCOP class classification

Indexed keywords

ALGORITHM; DATA SET; DECISION ANALYSIS; PREDICTION; PROBABILITY; PROTEIN;

ACCURACY; ARTICLE; PRIORITY JOURNAL; PROTEIN STRUCTURE; SUPPORT VECTOR MACHINE;

ALGORITHMS; ANIMALS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; PATTERN RECOGNITION, AUTOMATED; PROTEIN CONFORMATION; SEQUENCE ANALYSIS, PROTEIN;

EID: 45649085108     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2008.02.031     Document Type: Article

References (57)

1. Ambroise C., and McLachlan G.J. Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. 99 (2002) 6562-6566
2. Ayer M., Brunk H., Ewing G., Reid W., and Silverman E. An empirical distribution function for sampling with incomplete information. Ann. Math. Stat. 26 4 (1955) 641-647
3. Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U., Sackinger, E., Simard, P., Vapnik, V., 1994. Comparison of classifier methods: a case study in handwriting digit recognition. In: Proc. Int. Conf. Pattern Recognition, pp. 77-87.
4. Cai Y.-D., Liu X.-J., Xu X.-B., and Zhou G.-P. Support vector machines for predicting protein structural class. BMC Bioinform. 2 (2001) 3
5. Cai Y.-D., Liu X.-J., Xu X.-B., and Chou K.-C. Support vector machines for prediction of protein domain structural class. J. Theor. Biol. 221 (2003) 115-120
6. Cai Y.-D., Feng K.-Y., Lu W.-C., and Chou K.-C. Using logitboost classifier to predict protein structural classes. J. Theor. Biol. 238 (2006) 172-176
7. Cao Y., Liu S., Zhang L., Qin J., Wang J., and Tang K. Prediction of protein structural class with Rough sets. BMC Bioinform. 7 (2006) 20
8. Chai, H., Domeniconi, C., 2004. An evaluation of gene selection methods for multi-class microarray data classification. In: T. Scheffer (Ed.), Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics, pp. 3-10.
9. Chandonia J.M., Hon G., Walker N.S., Lo Conte L., Koehl P., Levitt M., and Brenner S.E. The ASTRAL compendium in 2004. Nucleic Acids Res. 32 (2004) D189-D192
10. Chang, C.-C., Lin, C.-J., 2001. LIBSVM: a library for support vector machines 2001. Software available at 〈http://www.csie.ntu.edu.tw/~cjlin/libsvm〉.
11. Chen C., Tian Y.-X., Zou X.-Y., Cai P.-X., and Mo J.-Y. Using pseudo-amino acid composition and support vector machine to predict protein structural class. J. Theor. Biol. 243 (2006) 444-448
12. Chen C., Zhou X., Tian Y., Zou X., and Cai P. Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Anal. Biochem. 357 (2006) 116-121
13. Chen, K.E., Lukasz, K., Jishou, R., 2007. Prediction of protein structural class using PSI-BLAST profile based collocation of amino acid pairs. The 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 17-20.
14. Chou K.C. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins 21 4 (1995) 319-344
15. Chou K.C. Prediction of protein structural classes and subcellular locations. Curr. Protein Pept. Sci. 1 2 (2000) 171-208
16. Chou K.-C., and Cai Y.-D. Predicting protein structural class by functional domain composition. Biochem. Biophys. Res. Commun. 321 (2004) 1007-1009
17. Chou P.Y., and Fasman G.D. Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. Relat. Areas Mol. Biol. 47 (1978) 45-148
18. Chou K.-C., and Maggiora G.M. Domain structural class prediction. Protein Eng. 11 7 (1998) 523-538
19. Chou K.C., and Zhang C.T. Predicting protein-folding types by distance functions that make allowances for amino-acid interactions. J. Biol. Chem. 269 (1994) 22014-22020
20. Crammer K., and Singer Y. On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2 (2001) 265-292
21. Ding Y.-S., Zhang T.-L., and Chou K.-C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept. Lett. 14 8 (2007) 811-815
22. Duan M.J., and Zhou Y.H. A contact energy function considering residue hydrophobic environment and its application in protein fold recognition. Genomics Proteomics Bioinformatics 3 4 (2005) 218-224
23. Duan K., Keerthi S.S., Chu W., Shevade S.K., and Poo A.N. Multi-category classification by soft-max combination of binary classifiers. In: Windeatt T., and Roli F. (Eds). MCS 2003, LNCS vol. 2709 (2003), Springer, Heidelberg 125-134
24. Duembgen, L., 2000. Available at 〈http://www.staff.unibe.ch/duembgen/software/#Isotone〉.
25. Feng K.Y., Cai Y.D., and Chou K.C. Boosting classifier for predicting protein domain structural class. Biochem. Biophys. Res. Commun. 334 1 (2005) 213-217
26. Guyon I., Weston J., Barnhill S., and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
27. Hastie T., Tibshirani R., and Friedman J. Elements of Statistical Learning: Data mining, Inference and Prediction (2001), Springer, New York
28. Hsu C.-W., and Lin C.-J. A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13 (2002) 415-425
29. Johnson Jr. W.C., and Macdonald J.R. Environmental features are important in determining protein secondary structure. Protein Sci. 10 (2001) 1172-1177
30. Kawashima S., Ogata H., and Kanehisa M. AAindex: amino acid index database. Nucleic Acids Res. 27 (1999) 368-369
31. Klein P., and Delisi C. Prediction of protein structural class from the amino-acid sequence. Biopolymers 25 (1986) 1659-1672
32. Kreßel U.H.-G. Pairwise classification and support vector machines. In: Scholkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in Kernel Methods: Support Vector Learning (1999), MIT press, Cambridge, MA, USA 255-268
33. Kurgan L.A., and Homaeian L. Prediction of structural classes for protein sequences and domains-impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy. Pattern Recognit. 39 12 (2006) 2323-2343
34. Lee Y., et al. Multicategory support vector machines: theory and application to the classification of microarray data and satellite radiance data. J. Am. Stat. Assoc. 99 (2004) 67-81
35. Levitt M., and Chothia C. Structural patterns in globular proteins. Nature 261 (1976) 552-557
36. Li W., and Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22 (2006) 1658-1659
37. Li T., Zhang C., and Ogihara M. A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20 (2004) 2429-2437
38. Lifson S., and Sander C. Antiparallel and parallel beta-strands differ in amino acid residue preferences. Nature 282 (1979) 109-111
39. Luo R.-Y., Feng Z.-P., and Liu J.-K. Prediction of protein structural class by amino acid and polypeptide composition. Eur. J. Biochem. 269 (2002) 4219-4225
40. Metfessel B.A., Saurugger P.N., Connelly D.P., and Rich S. Crossvalidation of protein structural class prediction using statistical clustering and neural networks. Protein Sci. 2 (1993) 1171-1182
41. Molinaro A.M., Simon R., and Pfeiffer R.M. Prediction error estimation: a comparison of resampling methods. Bioinformatics 21 15 (2005) 3301-3307
42. Murzin A.G., Brenner S.E., Hubbard T., and Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247 (1995) 536-540
43. Nakashima H., Nishikawa K., and Ooi T. The folding type of a protein is relevant to the amino acid composition. J. Biochem. 99 (1986) 153-162
44. Øhrn, A., 1999. Discernibility and rough sets in medicine: tools and applications. Ph.D. volume, Department of Computer and Information Science, Norwegian University of Science and Technology, Norway.
45. Pawlak, Z., 1991. Rough Sets: theoretical aspects of reasoning about data. In: Theory and Decision Library Series D, System Theory, Knowledge Engineering and Problem Solving, Kluwer Academic Publishers.
46. Platt J. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola A.J., Bartlett P.L., Scholkopf B., and Schuurmans D. (Eds). Advances in Large Margin Classifiers (2000), MIT Press, Cambridge 61-74
47. Pugalenthi G., Tang Ke., Suganthan P.N., Archunan G., and Sowdhamini R. A machine learning approach for the identification of odorant binding proteins from sequence-derived properties. BMC Bioinform. 8 (2007) 351
48. Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C.H., Angelo M., Ladd C., Reich M., Latulippe E., Mesirov J.P., et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. U.S.A. 98 (2001) 15149-15154
49. Rangwala H., and Karypis G. Building multiclass classifiers for remote homology detection and fold recognition. BMC Bioinform. 7 (2006) 455
50. Rifkin R., and Klautau A. In defense of one-vs-all classification. J. Mach. Learn. Res. 5 (2004) 101-141
51. Rifkin R., Mukherjee S., Tamayo P., Ramaswamy S., Yeang C.-H., Angelo M., Reich M., Poggio T., Lander E.S., Golub T.R., and Mesirov J.P. An analytical method for multiclass molecular cancer classification. Siam Rev. 45 4 (2003) 706-723
52. Vapnik V. Statistical Learning Theory (1998), Wiley-Interscience, New York, NY, USA
53. Wang Z.-X., and Yuan Z. How good is prediction of protein structural class by the component-coupled method?. Proteins: Struct. Funct. Genet. 38 (2000) 165-175
54. Weston, J., Watkins, C., 1999. Support vector machines for multiclass pattern recognition. In: Proceedings of the Seventh European Symposium on Artificial Neural Networks.
55. Zadrozny B., Elkan C., 2002. Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 694-699.
56. Zhang C.T., and Chou K.C. An optimization approach to predicting protein structural class from amino-acid composition. Protein Sci. 1 (1992) 401-408
57. Zhou G.P. An intriguing controversy over protein structural class prediction. J. Protein Chem. 17 8 (1998) 729-738