Protein sequences classification using radial basis function (RBF) neural networks

ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age

Volumn 2, Issue , 2002, Pages 764-768

  Wang, Dianhui ^a   Lee, Nung Kion ^b   Dillon, T S ^a   Hoogenraad, N J ^a  

^a LA TROBE UNIVERSITY   (Australia)

^b UNIVERSITI MALAYSIA SARAWAK   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; INFORMATION SCIENCE; NEURAL NETWORKS; PROTEINS; RADIAL BASIS FUNCTION NETWORKS;

CENTRALIZED INFORMATIONS; CLASSIFICATION CRITERION; CLASSIFICATION SYSTEM; INFORMATION RESOURCE; INTELLIGENT CLASSIFICATION; PERFORMANCE COMPARISON; PROTEIN SEQUENCE DATABASE; RADIAL BASIS FUNCTION NEURAL NETWORKS;

CLASSIFICATION (OF INFORMATION);

EID: 84964425182     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICONIP.2002.1198161     Document Type: Conference Paper

References (15)

1. Bishop C. M., Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995
2. H. C Wang, Dapazo J., L. G. De La Fraga, Y. P. Zhu, Carazo J. M., Self-organizing tree-growing network for the classification of protein sequences, Protein Science, (1998) 2613-2622
3. Ferran EA, Pflugfedder B, Ferrarap, Self Organized neural maps of human protein sequences, Protein Science, 3(1994) 507-521
4. S. Lawrence, I. Burns, Andrew Back, Ah Chung Tsoi, and C. Lee Giles, Neural Network Classification and Prior Class Probabilities, Neural Networks Tricks of the Trade, Lecture Notes in Computer Sciences, Springer, 1524 (1998)
5. I. Jonassen, Methods for finding motifs in sets of related biosequences, PhD Thesis, Department of Informatics, University of Bergen, (1996)
6. J. Moody and C. Darken, Faster learning in networks of locally-tuned processing units, Neural Comput., 1(1989) 281-294
7. NevProp-v3 http://www.scs.unr.edu/nevprop/
8. PIR, http://pir.georgetown.edu
9. Quilan, J. R. 1994, C4.5: programs for machine learning, San Mateo, CA Morgan Kaufmann
10. SAM: Sequence Alignment and Modeling Software System, Baskin Center for Computer Engineering and Science, http://www.cse.ucsc.edu/researchcompbio/sam.html
11. MEME: Multiple EM for Motif Elicitation UCSD Computer Science and Engineering http://meme.sdsc.edu
12. Wu, C. H., Berry, M., Shivakumar, S. & McLarty, J., Neural networks for full-scale protein sequence classification: sequence encoding with singular value decomposition, Machine Learning. 21(1995) 177-193
13. Wu C. H., Artificial neural networks for molecular sequence analysis. Computers Chemistry, 21(1997) 237-256
14. Y. S. Hwang, and Y. S. Bang. An Efficient Method to Construct a Radial Basis function Neural Network Classifier, Neural Networks, 10(1997) 1495-1503
15. David M.J. Tax, Martijin van Breukelen, Rober P.W. Duin, and Josef Kittler,Combining multiple classifiers by averaging or by multiplying?, Pattern Recognition 33(2000) 1475-1485.