International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 81 - Issue 15 |
Published: November 2013 |
Authors: Kshipra Chitode, Meghana Nagori |
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Kshipra Chitode, Meghana Nagori . A Comparative Study of Microarray Data Analysis for Cancer Classification. International Journal of Computer Applications. 81, 15 (November 2013), 14-18. DOI=10.5120/14198-2392
@article{ 10.5120/14198-2392, author = { Kshipra Chitode,Meghana Nagori }, title = { A Comparative Study of Microarray Data Analysis for Cancer Classification }, journal = { International Journal of Computer Applications }, year = { 2013 }, volume = { 81 }, number = { 15 }, pages = { 14-18 }, doi = { 10.5120/14198-2392 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2013 %A Kshipra Chitode %A Meghana Nagori %T A Comparative Study of Microarray Data Analysis for Cancer Classification%T %J International Journal of Computer Applications %V 81 %N 15 %P 14-18 %R 10.5120/14198-2392 %I Foundation of Computer Science (FCS), NY, USA
Cancer is most deadly human disease. According to WHO 7. 6 million deaths (around 13% of all deaths) in 2008 were caused by cancer. A Cancer diagnosis can be achieved with gene expression microarray data. Microarray allows monitoring of thousands of genes of a sample simultaneously. But all the genes in gene expression data are not informative. The relevant gene selection/extraction is the main challenge in microarray data analysis. Microarray data classification is two stage process i. e. features selection and classification. Feature selection techniques are used to extract a small subset of relevant genes without degrading the performance of classifier. The classifier uses these extracted relevant genes for cancer classification. In this review paper there is a comparative study of the feature selection and classification techniques. The evaluation criteria are applied to find out the best combination of feature selection and classification technique for accurate cancer classification