Research Article

A Combined Genetic Programming for Microarray Data Analysis

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

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Microarray Meta-genetic programming Majority voting Feature ranking

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