International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 80 - Issue 14 |
Published: October 2013 |
Authors: K. Umamaheswari, Dhivya. M, Chithra. S |
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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 }, year = { 2013 }, volume = { 80 }, number = { 14 }, pages = { 13-17 }, doi = { 10.5120/13928-1793 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2013 %A K. Umamaheswari %A Dhivya. M %A Chithra. S %T A Combined Genetic Programming for Microarray Data Analysis%T %J International Journal of Computer Applications %V 80 %N 14 %P 13-17 %R 10.5120/13928-1793 %I Foundation of Computer Science (FCS), NY, USA
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.