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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28179

Title: Identification of Promoter Sequences using Markov Chain and Relative Entropy
Authors: Cheng-Fa Cheng;Yi-Jen Chen
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: Bioinformatics;Promoter Prediction;Markov chain
Date: 2005-09
Issue Date: 2011-10-21T02:36:07Z
Publisher: 2005 Bioinformatics in Taiwan
Abstract: Abstract:First, we take DNA sequences of E. coli from the UCI Machine Learning Repository. We propose probability models which based on Markov chain and Kullback Leibler’s distance for solving the promoter prediction problems which include both TSS alignment and TSS un-alignment situations. When the transcription starting sites are given or sequence is not too short, our Markov-Kullback Score Method 1 can recognize and predict the RNA polymerase binding sites quickly. On the other hand if the transcription starting sites are not unknown or sequence is not long enough, the proposed Markov-Kullback Score Method 2 provides a powerful algorithm for promoter regions prediction.
URI: http://ntour.ntou.edu.tw/handle/987654321/28179
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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