Eukaryotic promoter prediction based on relative entropy and positional information

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Title Eukaryotic promoter prediction based on relative entropy and positional information
Author Wu, Shuanhu; Xie, Xudong; Liew, Alan Wee-Chung; Yan, Hong
Journal Name Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)
Year Published 2007
Place of publication United States
Publisher American Physical Society
Abstract The eukaryotic promoter prediction is one of the most important problems in DNA sequence analysis, but also a very difficult one. Although a number of algorithms have been proposed, their performances are still limited by low sensitivities and high false positives. We present a method for improving the performance of promoter regions prediction. We focus on the selection of most effective features for different functional regions in DNA sequences. Our feature selection algorithm is based on relative entropy or Kullback-Leibler divergence, and a system combined with position-specific information for promoter regions prediction is developed. The results of testing on large genomic sequences and comparisons with the PromoterInspector and Dragon Promoter Finder show that our algorithm is efficient with higher sensitivity and specificity in predicting promoter regions.
Peer Reviewed Yes
Published Yes
Publisher URI
Alternative URI
Volume 75
Issue Number 4
Page from 041908-1
Page to 041908-7
ISSN 1539-3755
Date Accessioned 2007-06-29
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Genome Structure; PRE2009-Pattern Recognition
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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