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Human Promoter Recognition Algorithm

Author: MeiLi
Tutor: LiWenJu
School: Liaoning Normal University
Course: Applied Computer Technology
Keywords: Human promoter recognition KL divergence CpG islands BP neural network SVM
CLC: Q75
Type: Master's thesis
Year: 2010
Downloads: 46
Quote: 1
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Abstract


After the completion of the human genome sketch , determine the genes and their regulatory networks become a challenging task . The promoter is an important component of the regulation of gene expression , has a key role in gene identification . The human promoter recognition technology has become the current hot research field , has a very important theoretical significance and application value . Based on a lot of reading literature , this study of human promoter recognition technology , two new human promoter recognition algorithm . Human promoter recognition algorithm based on KL divergence and BP neural network . The algorithm is applied first the KL divergence extraction resolution strongest hexamer , and its frequency of occurrence as the composition characterized ; then extract the characteristics of CpG islands , and combined with the composition characterized as the distinction between the promoter and the non - promoter region eigenvectors ; final application design promoter classifier , BP neural network technology . The classification by the promoter - outside exon classifier , promoter - intron classifiers and start sub -3'-UTR classifier composition , each category is a BP neural network , a comprehensive three classifiers results to identify the promoter sequence. Human promoter recognition algorithm based on two SVM classifier . The algorithm is applied Support Vector Machine technology design a two SVM classifier . First -class SVM classifier based on the characteristics of CpG islands DNA sequence classification , discriminant non - promoter sequence is fed to the second stage SVM classifier for further identification . Second stage SVM classifier consists of three SVM - sub-categories , namely, the promoter - exon SVM sub-categories , sub-classifiers promoter - intron SVM sub-categories , and promoter - 3' -UTR of SVM identification of each sub- classified according to the characteristics of the composition the promoter, the promoter sequence to the comprehensive prediction by the results of three subcategories . Finally two SVM classifier all promoter sequences identified as the final experimental result . The experimental results show that the above algorithm proposed in this paper is effective , has a high sensitivity and specificity .

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CLC: > Biological Sciences > Molecular Biology > Molecular Genetics
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