Machine learning with automatic feature selection for multi-class protein fold classification

Journal of Information Science and Engineering

Volumn 21, Issue 4, 2005, Pages 711-720

  Huang, Chuen Der ^a   Liang, Sheng F U ^b   Lin, Chin Teng ^b   Ruei Cheng, W U ^b  

^a HSIUPING INSTITUTE OF TECHNOLOGY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Bioinformatics; Feature selection; Gate; Hierarchical architecture; Machine learning; Neural network; Protein fold

Indexed keywords

AMINO ACIDS; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; PERFORMANCE; PROTEINS;

BIOINFORMATICS; FEATURE SELECTION; GATE; HIERARCHICAL ARCHITECTURE; PROTEIN FOLD;

FEATURE EXTRACTION;

EID: 22144478633     PISSN: 10162364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (12)

1. I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S. H. Kim, "Recognition of a protein fold in the context of the SCOP classification," PROTEINS: Structure, Function and Genetics, Vol. 35, 1999, pp. 401-407.
2. P. Y. I. Chou and G. D. Fashman, (eds.), Prediction of Protein Structure and Principles of Protein Conformation, Plenum Press, New York, 1989, pp. 549-586.
3. H. Nakashima, K. Nishikawa, and T. Ooi, "The folding type of a protein is relevant to the amino acid composition," Journal of Biochem, Vol. 99, 1986, pp. 152-162.
4. I. Dubchak, S. R. Holbrook, and S. H. Kim, "Prediction of protein folding class from amino acid composition," Proteins, Vol. 16, 1993, pp. 79-91.
5. I. Dubchak, I. Muchnik, S. R. Holbrook, and S. H. Kim, "Prediction of protein folding class using global description of amino acid sequence," in Proceeding of National Academia (Biophysics), Vol. 92, 1995, pp. 8700-8704.
6. R. De, N. R. Pal, and S. K. Pal, "Feature analysis: neural network and fuzzy set theoretic approaches," Pattern Recognition, Vol. 30, 1997, pp. 1579-1590.
7. K. Fukunaga and W. Koontz, "Applications of the karhunen-loeve expansion to feature selection and ordering," IEEE Transactions on Computers, Vol. C-19, 1970, pp. 311-318.
8. K. L. Priddy, S. K. Rogers, D. W. Ruck, G. L. Tarr, and M. Kabrisby, "Bayesian selection of important features for feed-forward neural network," NeuroComputing, Vol.5, 1993, pp. 91-103.
9. A. Verikas and M. Bacauskiene, "Feature selection with neural networks," Pattern Recognition Letters, Vol. 23, 2002, pp. 1323-1335.
10. N. R. Pal and K. Chintalapudi, "Connectionist system for feature selection," Neural, Parallel and Scientific Computation, Vol. 5, 1997, pp. 359-381.
11. I. F. Chung, C. D. Huang, Y. H. Shen, and C. T. Lin, "Recognition of structure classification of protein folding by NN and SVM hierarchical learning architecture," International Conference on Neural Information Processing (ICONIP '03), 2003, pp. 1159-1167.
12. I. Dubchak and C. H. Q. Ding, "Multi-class protein fold recognition using support vector machines and neural networks," Bioinformatics, Vol. 17, 2001, pp. 349-358.