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A Comparison Study on SVM MDR and D-MDR for Detecting Gene-Gene Interaction in Continuous Data
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 Title & Authors
A Comparison Study on SVM MDR and D-MDR for Detecting Gene-Gene Interaction in Continuous Data
Lee, Jong-Hyeong; Lee, Jea-Young;
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 Abstract
We have used a multifactor dimensionality reduction(MDR) method to study the major gene interaction effect in general; however, without application of the MDR method in continuous data. In light of this, many methods have been suggested such as Expanded MDR, Dummy MDR and SVM MDR. In this paper, we compare the two methods of SVM MDR and D-MDR. In addition, we identify the gene-gene interaction effect of single nucleotide polymorphisms(SNPs) associated with economic traits in Hanwoo(Korean cattle). Lastly, we discuss a new method in consideration of the advantages that the other methods present.
 Keywords
Gene-gene interaction;MDR method;SNP;SVM algorithm;Dummy MDR;Multifactor Dimensionality Reduction(MDR);SVM;
 Language
Korean
 Cited by
 References
1.
Cho, D. (2010). Mixed-effects LS-SVM for longitudinal data, Journal of Korean Data & Information Science Society, 21, 363-369.

2.
Chung, Y. J., Lee, S. Y. and Park, T. S. (2005). Multifactor dimensionality reduction in the presence of missing observations, 2005 Proceedings of the Autumn Conference, Korea Statistical Society, 31-36.

3.
Good, P. (2000). Permutation Test: A practical guide to resampling method for testing hypotheses, Springer-Verlag Berlin and Heidelberg GmbH & Co., New York.

4.
Lee, H. G. (2009a). Power of multifactor dimensionality reduction with dummy variable and detecting best gene interaction, M.S. Thesis, 1-53.

5.
Lee, J. Y. and Lee, J. H. (2010). Support vector machine and multifactor dimensionality reduction for detecting major gene interactions of continuous data, Journal of Korean Data & Information Science Society, 21, 1271-1280.

6.
Lee, Y. S. (2009b). Study on the identification of candidate genes and their haplotypes that are associated with growth and carcass traits in the QTL region of BTA6 in a Hanwoo population, Ph. D. Thesis, 1-94.

7.
Ritchie, M. D., Hahn, L. W., Roodi, N., Bailey, L. R., Dupont, W. D., Parl, F. F. and Moore, J. H. (2001). Multifactor-dimensionality reduction reveals high-order interactions among estrogen- metabolism genes in sporadic breast cancer, American Journal of Human Genetics, 69, 138-147. crossref(new window)

8.
Scholkopf, B. and Smola, A. J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.

9.
Shim, J., Park, H. and Seok, K. H. (2009). Variance function estimation with LS-SVM for replicated data, Journal of Korean Data & Information Science Society, 20, 925-931

10.
Snelling, W. M., Casas, E., Stone, R. T., Keele, J. W., Harhay, G. P., Benett, G. L. and Smith, T. P. L. (2005). Linkage mapping bovine EST-based SNP, BioMed Central Genomics, 6, 74-84.

11.
Vapnik, V. (1998). Statistical Learning Theory, John Wiley & Sons, New York.

12.
Tan, P., Steinbach, M. and Kumar, V. (2006). Introduction to Data Mining, Addison-Wesley, New-York.