Machine learning for multi-class protein fold classification based on neural networks with feature gating

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2714, Issue , 2003, Pages 1168-1175

  Huang, Chuen Der ^b,c   Chung, I Fang ^b   Pal, Nikhil Ranjan ^a   Lin, Chin Teng ^b  

^a INDIAN STATISTICAL INSTITUTE   (India)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^c HSIUPING INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; FEATURE EXTRACTION; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS;

BIOINFORMATICS APPLICATIONS; CLASSIFICATION SYSTEM; COMPUTATION TIME; FOLDING PROCESS; HIERARCHICAL ARCHITECTURES; NEURAL CLASSIFIERS; PROTEIN FOLD CLASSIFICATIONS; TEST ACCURACY;

CLASSIFICATION (OF INFORMATION);

EID: 33646418489     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (12)

1. I. Dubchak, I. Muchnik, S. R. Holbrook and S-H Kim, "Prediction of protein folding class using global description of amino acid sequence", Proc. Natl. Acad. (Biophysics) USA, Vol. 92, pp. 8700-8704, Sep., 1995.
2. 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, pp. 401-407, 1999.
3. P. Y. In Chou and G. D. Fashman, editor, "Prediction of protein structure and principles of protein conformation". Plenum Press, New York:, pp. 549-586, 1989.
4. H. Nakashima, K. Nishikawa, and T. Ooi ,"The folding type of a protein is relevant to the amino acid composition", J. Biochem, Vol. 99, pp. 152-162, 1986.
5. I. Dubchak, S. R. Holbrook, and S. H. Kim, "Prediction of protein folding class from amino acid composition", PROTEINS: Structure, Function and Genetics, Vol. 16, pp. 79-91, 1993.
6. N. R. Pal and K. Chintalapudi, "Connectionist system for feature selection," Neural, Parallel and Scientific Computation, Vol. 5, No. 3, pp. 359-381, 1997.
7. R. De, N. R. Pal, and S. K. Pal, "Feature analysis: neural network and fuzzy set theoretic approaches", Pattern Recognition, Vol. 30, No. 10, pp. 1579-1590, 1997.
8. K. Fukunaga and W. Koontz, "Applications of the karhunen-Loeve expansion to feature selection and ordering", IEEE Trans. Comp., Vol. C-19, 1970.
9. 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, pp. 91-103, 1993.
10. A. Verikas and M. Bacauskiene, "Feature Selection with Neural Networks," Pattern Recognition Letter, Vol. 23, pp. 1323-1335, 2002.
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", Int. Conf. Neural Infor. Processing, ICONIP'03, Turkey, 2003.
12. I. Dubchak and C. H. Q. Ding, "Multi-class protein fold recognition using support vector machines and neural networks," Bioinformatics, Vol. 17, No. 4, pp. 349-358, 2001.