[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/IJFIS.2004.4.2.231

Negative Selection Algorithm for DNA Sequence Classification

Lee, Dong Wook (School of Electrical and Electronic Engineering, Chung-Ang University)
Sim, Kwee-Bo (School of Electrical and Electronic Engineering, Chung-Ang University)

Publication Information

International Journal of Fuzzy Logic and Intelligent Systems / v.4, no.2, 2004 , pp. 231-235 More about this Journal

Abstract

According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.

Keywords

negative selection; pattern classification; DNA; artificial immune system;

Citations & Related Records

Reference

1	R. A. Wallace, G. P. Sanders, and R. J. Ferl, BIOLOGY: The Science of Life, 3rd edition, HarperCollins Publishers Inc., 1991
2	I. Roitt, J. Brostoff, D. Male, Immunology, 4th edition, Mosby, 1996
3	P. Baldi, S. Brunak, Bioinformatics: The Machine Learning Approach, MIT Press, Cambridge, Mass., 2001
4	M. Gelfand, 'Prediction of function in DNA sequence analysis,' J. Computational Biology, vol. 1, pp. 87-115, 1995
5	K. B. Hwang, D. Y. Cho, S. W. Park, S. D. Kim, and B. T. Zhang, 'Applying machine learning techniques to analysis of gene expression data: Cancer diagnosis,' in Methods of Microarray Data Analysis, Kluwer Academic Publishers, pp. 167-182, 2002
6	F. Esponda, S. Forrest, anti P. Helman, 'Formal framework for positive and negative detection scheme,' IEEE Trans. Syst., Man, Cybern., Part B, vol. 34, no. 1, pp.357-373, Feb. 2004 DOI ScienceOn
7	D. Dasgupta, S. Forrest, 'An anomaly detection algorithm inspired by the immune system,' in Artificial Immune Systems and Their Applications, D. Dasgupta, Ed., Springer, pp. 262-276, 1999
8	G. B. Fogel, K. Chellapilla, and D. B. Fogel, 'Identification of coding regions in DNA sequences using evolved neural networks,' in Evolutionary Computation in Bioinformatics, G. B. Fogel and D. W. Corne, Eds. San Francisco, CA: Morgan Kaufmann, pp. 196-218, 2003
9	Comparative RNA Web Site, URL:http://www.rna. icmb.utexas.edu/
10	K. B. Sim and D. W. Lee, 'Self-nonself recognition algorithm based on positive and negative selection,' IEICE Trans. Info. & Syst., vol. E87-D, no. 2, Feb. 2004
11	J. Kim, and P. J. Bentley, 'Towards an artificial immune system for network intrusion detection: An investigation of clonal selection with a negative selection operator,' Proc. Congr. Evolutionary Computation, pp. 1244-1252, 2001
12	P. D'haeseleer, S. Forrest, and P. Helman, 'An immunological approach to change detection: algorithms, analysis, and implications,' Proc. IEEE Symp. Computer Security Privacy, pp. 110-119, 1996
13	S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri 'Self-nonself discrimination in a computer,' Proc. IEEE Symposium on Research in Security and Privacy, pp. 202-212, 1994