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基于迭代判别分析的遗传算法识别真核生物启动子。

Recognition of eukaryotic promoters using a genetic algorithm based on iterative discriminant analysis.

作者信息

Levitsky Victor G, Katokhin Alexey V

机构信息

Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.

出版信息

In Silico Biol. 2003;3(1-2):81-7. Epub 2003 Feb 27.

PMID:12762848
Abstract

A new approach to recognizing promoter regions of eukaryotic genes is proposed and illustrated by an example of Drosophila melanogaster. The essence of its novelty is in realizing the genetic algorithm to search for optimal partition of a promoter region into local nonoverlapping fragments and selection of the most significant dinucleotide frequencies for the fragments obtained. The method developed was applied to recognizing TATA-containing (TATA+) and DPE-containing (DPE+) promoters of Drosophila melanogaster genes. The program for promoter recognition is included into the GeneExpress system, section RegScan (http://wwwmgs.bionet.nsc.ru/mgs/programs/proga/).

摘要

本文提出了一种识别真核基因启动子区域的新方法,并以黑腹果蝇为例进行了说明。其新颖之处在于实现了一种遗传算法,用于搜索启动子区域到局部非重叠片段的最优划分,并为所得片段选择最显著的二核苷酸频率。所开发的方法被应用于识别黑腹果蝇基因中含TATA(TATA+)和含DPE(DPE+)的启动子。启动子识别程序包含在GeneExpress系统的RegScan部分(http://wwwmgs.bionet.nsc.ru/mgs/programs/proga/)。

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Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.原核生物和真核生物启动子预测的计算工具的批判性评估。
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbab551.
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Eukaryotic and prokaryotic promoter prediction using hybrid approach.使用混合方法进行真核和原核启动子预测。
Theory Biosci. 2011 Jun;130(2):91-100. doi: 10.1007/s12064-010-0114-8. Epub 2010 Nov 3.
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Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions.
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BMC Bioinformatics. 2007 Dec 19;8:481. doi: 10.1186/1471-2105-8-481.
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Statistical extraction of Drosophila cis-regulatory modules using exhaustive assessment of local word frequency.利用局部词频的详尽评估对果蝇顺式调控模块进行统计提取。
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