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Eukaryotic and prokaryotic promoter prediction using hybrid approach.
MedLine Citation:
PMID:  21046474     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Promoters are modular DNA structures containing complex regulatory elements required for gene transcription initiation. Hence, the identification of promoters using machine learning approach is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods have been proposed for the prediction of eukaryotic and prokaryotic promoters. However, the performances of these methods are still far from being satisfactory. In this article, we develop a hybrid approach (called IPMD) that combines position correlation score function and increment of diversity with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. By applying the proposed method to Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli, and Bacillus subtilis promoter sequences, we achieve the sensitivities and specificities of 90.6 and 97.4% for D. melanogaster, 88.1 and 94.1% for H. sapiens, 83.3 and 95.2% for C. elegans, 84.9 and 91.4% for E. coli, as well as 80.4 and 91.3% for B. subtilis. The high accuracies indicate that the IPMD is an efficient method for the identification of eukaryotic and prokaryotic promoters. This approach can also be extended to predict other species promoters.
Authors:
Hao Lin; Qian-Zhong Li
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Publication Detail:
Type:  Journal Article     Date:  2010-11-03
Journal Detail:
Title:  Theory in biosciences = Theorie in den Biowissenschaften     Volume:  130     ISSN:  1611-7530     ISO Abbreviation:  Theory Biosci.     Publication Date:  2011 Jun 
Date Detail:
Created Date:  2011-05-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9708216     Medline TA:  Theory Biosci     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  91-100     Citation Subset:  IM    
Affiliation:
Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China, hlin@uestc.edu.cn.
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