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KMID : 0613820070170050714
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Journal of Life Science
2007 Volume.17 No. 5 p.714 ~ p.718
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A Study On the Application Methods of a Support Vector Machine for Gene Promoter Prediction
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Kim Ki-Bong
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Abstract
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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.
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KEYWORD
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high-throughput sequencing, promoter prediction, genetic networks, support vector machine, encoding method
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