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Microarray Data Classification Using Support Vector Machine
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International Journal of Biometrics and Bioinformatics (IJBB)
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Volume:  5    Issue:  1
Pages:  1-27
Publication Date:   March / April 2011
ISSN (Online): 1985-2347
Pages 
10 - 15
Author(s)  
Seeja K.R. - India
Shweta - India
 
Published Date   
04-04-2011 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Support Vector Machines, Microarray, Classification 
 
 
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DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network. 
 
 
 
1 Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gassenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring”, Science, 286(15):531–537, 1999.
2 Terrence S. Furey, Nello Cristianini, Nigel Duffy, David W. Bednarski, Michèl Schummer, and David Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data “, Bioinformatics6(10): 906-914 , 2000
3 Zhang, X. and Ke, H.,” ALL/AML cancer classification by gene expression data using SVM and CSVM approach”, Genome Informatics, Universal Academy Press, pp. 237- 239, 2000
4 Xin Zhao, Leo Wang-Kit Cheung, “Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data”, BMC Bioinformatics.,8:67,2007.
5 Wenlong Xu, Minghui Wang, Xianghua Zhang, Lirong Wang, Huanqing Feng,” SDED: A novel filter method for cancer-related gene selection”, Bioinformation 2(7): 301-303,2008.
6 D.P. Berrar, C.S. Downes, W. Dubitzky, “Multiclass Cancer Classification Using Gene Expression Profiling and Probabilistic Neural Networks”, Pacific Symposium on Biocomputing 8:5-16, 2003.
7 Pang-Ning Tan, Michal Steinbach, Vipin Kumar, “Introduction to Data Mining.”,Pearson Education Inc., pp. 256-276, 2009
8 Vapnik V , The nature of statistical learning theory. 2nd edition. Springer,1999
9 Joachims, T., “Making large-scale SVM learning practical”, Advances in Kernel Methods – Support Vector Learning, B. Schokopf et al. (ed.), MIT Press, 1999.
10 Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G, “Support Vector Machines and Kernels for Computational Biology.”, PLoS Comput Biol 4(10), 2008.
11 ALL/AML Bench Mark Dataset:
12 www.broadinstitute.org/cgibin/ cancer/publications/pub_paper.cgi?mode=view&paper_id=43
13 Platt, J. C.,” Fast training of support vector machines using sequential minimal optimization”. Advances in kernel methods: Support vector machines, B. Schokopf et al. (ed.), MIT Press, 1999.
14 WEKA software: www.cs.waikato.ac.nz/~ml/WEKA
 
 
 
1 D. A. Salem, R. A. Abul Seoud and H. A. Ali, (2011), “DMCA: A Combined Data Mining Technique for Improving the Microarray Data Classification Accuracy” in Proceedings of International Conference on Environment and BioScience, IPCBEE vol.21 (2011) © (2011) IACSIT Press, Singapore, 2011, pp. 36-41.
2 D. A. Salem, R. A. Abul and H. A. Ali, “MGS-CM: A Multiple Scoring Gene Selection Technique for Cancer Classification using Microarrays”, International Journal of Computer Applications, 36(6), pp. 30-37. December 2011.
3 D. A. Salem, R. A. A. A. Abul Seoud, and H. A. Ali, “A New Gene Selection Technique Based on Hybrid Methods for Cancer Classification Using Microarrays”, International Journal of Bioscience, Biochemistry and Bioinformatics, 1(4), pp. 261-266. November 2011.
 
 
 
 
 
Seeja K.R. : Colleagues
Shweta : Colleagues  
 
 
 
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