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http://dx.doi.org/10.5352/JLS.2007.17.5.714

A Study On the Application Methods of a Support Vector Machine for Gene Promoter Prediction.  

Kim, Ki-Bong (Department of Bioinfomatics Engineering, Sangmyung University)
Publication Information
Journal of Life Science / v.17, no.5, 2007 , pp. 714-718 More about this Journal
Abstract
The high-throughput sequencing of a lot of genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable attention in recent years since exact promoter prediction can give a clue to the elucidation of overall genetic networks. In this study, applications of support vector machine(SVM) to promoter prediction are explored to show a right approaches to discriminate between promoter and non-promoter regions by means of SVM. The results of various experiments show that encoding method, encoding region and learning data constitution can play an important role in the performance of SVM.
Keywords
high-throughput sequencing; promoter prediction; genetic networks; support vector machine; encoding method;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Joachims, T. 1999. Advances in Kernel Methods - Support Vector Learning. pp. 169-184, MIT Press. Cambridge, MA USA
2 Jung, M., W. Park and K. Kim. 2004. Development of integrated system for motif and domain search. Journal of Life Science 14(6), 991-996   과학기술학회마을   DOI   ScienceOn
3 Jung, M., W. Park and K. Kim. 2004. Development of web-based assistant system for protein-protein interaction and function analysis. Journal of Life Science 14(6), 997-1002   과학기술학회마을   DOI   ScienceOn
4 Kulp, D, D. Haussler, M.G. Reese and F. H. Eeckman. 1996. A generalized Hidden Markov Model for the recognition of human genes in DNA. Proc Int Conf Intell Syst Mol Biol. 4, 134-42
5 Perier, R. C., V. Praz, T. Junier, C. Bonnard and P. Bucher . 2000. The Eukaryotic Promoter Database (EPD). Nucleic Acids Research 28, 302-303   DOI   ScienceOn
6 SVMlight, http://svmlight.joachims.org/
7 Zhang, Y., C. Chu, Y. Chen, H. Zha and X. Ji. 2006. Splice site prediction using support vector machines with a Bayes kernel. Expert Systems with Applications 30, 73-81   DOI   ScienceOn
8 http://www.integratedgenomics.com
9 Fickett, J. W. and A. C. Hatzigeorgiou. 1997. Eukaryotic promoter recognition. Genome Res. 7, 839-844
10 Fofanov, Y., Y. Luo, C. Katili, J. Wang, Y. Belosludtsev, T. Powdrill, C. Belapurkar, V. Fofanov, T. Li, S. Chumakov and B. Pettitt. 2004. How independent are the appearance of n-mers in different genomes. Bioinformatics 20, 2421-2428   DOI   ScienceOn
11 Gangal, R. and P. Sharma. 2005. Human pol II promoter prediction: time series descriptors and machine learning. Nucleic Acids Res. 33(4), 1332-1336   DOI   ScienceOn
12 Gordon, L., A. Chervonenkis, A. Gammerman, I. Shahmuradov and V. Solovyev. 2003. Sequence alignment kernel for recognition of promoter regions. Bioinformatics 19(15), 1964-1971   DOI   ScienceOn