A novel approach to extracting features from motif content and protein composition for protein sequence classification

Neural Networks

Volumn 18, Issue 8, 2005, Pages 1019-1028

  Zhao, Xing Ming ^a,b   Cheung, Yiu Ming ^c   Huang, De Shuang ^a  

^a INSTITUTE OF INTELLIGENT MACHINES   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Genetic algorithm; Motif content; Protein composition; Protein sequence classification; Support vector machine

Indexed keywords

CORRELATION METHODS; EIGENVALUES AND EIGENFUNCTIONS; GENETIC ALGORITHMS; MATRIX ALGEBRA; OPTIMIZATION; PARAMETER ESTIMATION; VECTORS;

MOTIF CONTENT; PROTEIN COMPOSITION; PROTEIN SEQUENCE CLASSIFICATION; SUPPORT VECTOR MACHINE;

PROTEINS;

AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTICLE; COMPARATIVE STUDY; EXTRACTION; GENETIC ALGORITHM; PHAGE DISPLAY; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN MOTIF; SEQUENCE ANALYSIS;

AMINO ACID MOTIFS; ANIMALS; ARTIFICIAL INTELLIGENCE; DATABASES, PROTEIN; HUMANS; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; PROTEINS; ROC CURVE; SEQUENCE ANALYSIS, PROTEIN; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 26944462332     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2005.07.002     Document Type: Article

References (36)

1. S. Altschul, T. Madden, A. Schafer, J. Zhang, Z. Zhang, and W. Miller Gapped blast and psi-blast: A new generation of protein data Nucleic Acids Research 25 1997 3389 3402
2. W. Barker, J. Garavelli, H. Huang, P. McGarvey, B. Orcutt, and G. Srinivasarao The protein information resource (PIR) Nucleic Acids Research 28 2000 41 44
3. A. Ben-Hur, and D. Brutlag Remote homology detection: A motif based approach Bioinformatics 19 2003 26 33
4. R. Brennan, and B. Matthews The helix-turn-helix DNA binding motif (minireview) Journal of Biological Chemistry 264 1989 1903 1906
5. S. Brenner, P. Koehl, and M. Levitt The ASTRAL compendium for sequence and structure analysis Nucleic Acids Research 28 2000 254 256
6. Deb, K., & Reddy, A. (2003). Classification of two and multi-class cancer data reliably using multi-objective evolutionary algorithms. KanGAL Report, No. 2003006.
7. Deb, K., & Reddy, A. (2003). Classification of two-class cancer data reliably using evolutionary algorithms. KanGAL Report, No. 2003001.
8. K. Deb, and A.R. Reddy Large-scale scheduling of casting sequences using a customized genetic algorithm Lecture Notes in Computer Science 2936 2004 141 152
9. I. Diamantaras, and S. Kung Principal component neural networks, theory and applications 1996 Wiley London
10. C. Ding, and I. Dubchak Multi-class protein fold recognition using support vector machines and neural networks Bioinformatics 17 2001 349 358
11. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison Biological sequence analysis: Probabilistic models of proteins and nucleic acids 1998 Cambridge University Press
12. D.R. Eads, D. Hill, S. Davis, S.J. Perkins, J. Ma, and R.B. Porter Genetic algorithms and support vector machines for time series classification Proceedings of SPIE 4787 2002 74 85
13. L. Edler, J. Grassmann, and S. Suhai Role and results of statistical methods in protein fold class prediction Mathematical and Computer Modelling 33 2001 1401 1417
14. L. Falquet, M. Pagni, P. Bucher, N. Hulo, C. Sigrist, and K. Hofmann The PROSITE database, its status in 2002 Nucleic Acids Research 30 2002 235 238
15. F. Friedrichs, and C. Igel Evolutionary tuning of multiple SVM parameters Proceedings of the 12th European Symposium on Artificial Neural Networks, Bruges, Belgium 2004 pp. 519-524
16. F. Friedrichs, and C. Igel Evolutionary tuning of multiple SVM parameters Neurocomputing 64 2005 107 117
17. H. Fröhlich, O. Chapelle, and B. Schölkopf Feature selection for support vector machines by means of genetic algorithms Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento, California, USA 2003 pp. 142-148
18. M. Gribskov, A. McLachlan, and D. Eisenberg Profile analysis: Detection of distantly related proteins Proceedings of the national academy of sciences of the USA Vol. 84 1987 pp. 4355-4358
19. S. Henikoff, and J. Henikoff Position-based sequence weights Journal of Molecular Biology 243 1994 574 578
20. Y. Hou, W. Hsu, M.L. Lee, and C. Bystroff Efficient remote homology detection using local structure Bioinformatics 19 2003 2294 2301
21. C. Igel Multi-objective model selection for support vector machines Proceedings of the third international conference on evolutionary multi-criterion optimization, Mexico 2005 pp. 534-546
22. T. Jaakkola, M. Diekhans, and D. Haussler A discriminative framework for detecting remote protein homologies Journal of Computational Biology 7 2000 95 114
23. I. Jolliffe Principal component analysis 1986 Springer-Verlag New York
24. K. Jong, E. Marchiori, and A. van der Vaart Analysis of proteomic pattern data for cancer detection Lecture Notes in Computer Science 3005 2004 41 51
25. A. Krogh, M. Brown, I. Mian, K. Sjolander, and D. Haussler Hidden markov models in computational biology: Applications to protein modeling Journal of Molecular Biology 235 1994 1501 1531
26. C. Leslie, E. Eskin, A. Cohen, J. Weston, and W.S. Noble Mismatch string kernels for discriminative protein classification Bioinformatics 20 2004 467 476
27. L. Liao, and W.S. Noble Combining pairwise sequence similarity and support vector machines for detecting remoteprotein evolutionary and structural relationships Journal of Computational Biology 10 2003 857 868
28. F. Markowetz, L. Edler, and M. Vingron Support vector machines for protein fold class prediction Biometrical Journal 45 3 2003 377 389
29. A. Murzin, S. Brenner, T. Hubbard, and C. Chothia SCOP: A structural classification of protein databases for investigation of sequences and structures Journal of Molecular Biology 247 1995 536 540
30. J.R. Quinlan C4.5: Programs for empirical learning 1993 Morgan Kaufmann San Francisco, CA
31. M. Raymer, W. Punch, E. Goodman, L. Kuhn, and A. Jain Dimensionality reduction using genetic algorithms IEEE Transaction on Evolutionary Computation 4 2000 164 171
32. T.P. Runarsson, and S. Sigurdsson Asynchronous parallel evolutionary model selection for support vector machines Neural Information Processing - Letters and Reviews 3 3 2004 59 68
33. V. Vapnik The nature of statistical learning theory 1995 Springer-Verlag Berlin
34. X. Wang, D. Schroeder, D. Dobbs, and V. Honavar Automated data-driven discovery of motif-based protein function classifiers Information Sciences 155 2003 1 18
35. J. Yang, and V. Honavar Feature subset selection using a genetic algorithm IEEE Intelligent Systems 13 1 1998 44 49
36. X.M. Zhao, D.S. Huang, Y.M. Cheung, H.Q. Wang, and X. Huang A novel hybrid GA/SVM system for protein sequences classification Lecture Notes in Computer Science 3177 2004 11 16