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Reseach Article

A Combined Genetic Programming for Microarray Data Analysis

by K. Umamaheswari, Dhivya. M, Chithra. S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 14
Year of Publication: 2013
Authors: K. Umamaheswari, Dhivya. M, Chithra. S
10.5120/13928-1793

K. Umamaheswari, Dhivya. M, Chithra. S . A Combined Genetic Programming for Microarray Data Analysis. International Journal of Computer Applications. 80, 14 ( October 2013), 13-17. DOI=10.5120/13928-1793

@article{ 10.5120/13928-1793,
author = { K. Umamaheswari, Dhivya. M, Chithra. S },
title = { A Combined Genetic Programming for Microarray Data Analysis },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 14 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number14/13928-1793/ },
doi = { 10.5120/13928-1793 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:32.097258+05:30
%A K. Umamaheswari
%A Dhivya. M
%A Chithra. S
%T A Combined Genetic Programming for Microarray Data Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 14
%P 13-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Microarray technology is a powerful tool to monitor gene expression or gene expression changes of hundreds or thousands of genes in a single experiment. Meta-Genetic Programming is the meta learning technique of evolving a genetic programming system to predict cancer classes for better understanding of different types of cancers and to find the possible biomarkers for diseases. A new technique which is known as Majority Voting Genetic Programming Classifier (MVGPC) combined with meta-genetic programming (MGP) is proposed which combines meta-genetic programming and majority voting technique to predict the cancer class for a given patient sample with higher accuracy and minimum computational time. This paper also aims to provide a means to identify cancer at an early stage and hence increase the chances of survival for the patients.

References
  1. J. R. Koza, "Genetic Programming: On the Programming of Computers by Means of Natural Selection. " The MIT Press, 1992.
  2. T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov,H. Coller, M. Loh, J. Downing, M. Caligiuri, C. Bloomfield, and E. Lander, "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring," Science, vol. 286, no. 15, pp. 531–537, 1999.
  3. V. Vapnik, "The Nature of Statistical Learning Theory" NewYork, USA: Springer-Verlag, 1995.
  4. B. Dasarathy, "Nearest Neighbor(NN) Norms: NN Pattern Classification Techniques",IEEE Computer Society Press, 1991.
  5. Bruce Edmonds, "Meta-Genetic Programming: Co-evolving the Operators of Variation", Turkish Journal of Electrical Engineering, Vol. 9, November 2001
  6. Topon Kumar Paul and Hitoshi Iba, "Prediction of Cancer Class with Majority Voting Genetic Programming Classifier using Gene Expression Data,"IEEE/ ACM Transactions on Computational Biology and Bioinformatics, Vol. 6, no. 2, pp. 353-367, April- June 2009
  7. L. Kuncheva and C. Whitaker, "Measures of diversity in classifier ensembles and their relationships with the ensemble accuracy," Machine Learning, vol. 51, pp. 181–207, 2003.
  8. B. Matthews, "Comparison of the predicted and observed secondarystructure of T4 phage lysozyme," Biochemica et Biophysica Acta. , vol. 405, pp. 442–451, 1975.
  9. Golub, T. et al. (1999)" Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring" Science, 286, 531–537.
  10. Alizadeh, A. et al. (2000)" Distinct types of diffuse large B-cell Lymphoma Identified by gene expression profiling", Nature, 4051, 503–511.
  11. Ovary Gene, URL: http://public. gnf. org/cancer/ovary/ovary. htm
  12. Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff," A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 9, No. 4, July/August 2012
Index Terms

Computer Science
Information Sciences

Keywords

Microarray Meta-genetic programming Majority voting Feature ranking