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首页> 外文会议>Information Technology and Applications, 2005. ICITA 2005. Third International Conference on >Integration of knowledge-discovery and artificial-intelligence approaches for promoter recognition in DNA sequences
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Integration of knowledge-discovery and artificial-intelligence approaches for promoter recognition in DNA sequences

机译：整合知识发现和人工智能方法以识别DNA序列中的启动子

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摘要

Bioinformatics nowadays is a very attractive field. Many fascinating biological problems were still unsolved, even after a great amount of diverse genomic sequences have been sequenced for the coming of post genome era. Currently available programs are far from powerful enough to recognize the regulatory signals completely. Researches have looked for various types of patterns around the transcription start site (TSS) and tried to translate those as classification rules; however, they were not always good solutions. In this paper, we proposed a new hybrid learning system to recognize the regulatory elements (i.e., promoter) in deoxyribonucleic acid (DNA) sequences. The proposed hybrid system calculated the distributions of oligo-nucleotides statistics as positional weight matrices which contribute to discriminate promoters from non-promoters. This study can help to locate the expressive regions of DNA, to foretell and to realize the properties, structures, and functions of the proteins that are synthesized starting from the coding region of DNA. The benchmark datasets were evaluated using the leave-one-out method. The experimental results demonstrate that the proposed system has higher accuracy than others.
机译：如今，生物信息学是一个非常有吸引力的领域。即使已经为后基因组时代的到来测序了大量多样的基因组序列，许多引人入胜的生物学问题仍未解决。当前可用的程序远不够强大，无法完全识别监管信号。研究已经在转录起始位点（TSS）周围寻找了各种类型的模式，并试图将其翻译为分类规则。但是，它们并不总是好的解决方案。在本文中，我们提出了一种新的混合学习系统，以识别脱氧核糖核酸（DNA）序列中的调控元件（即启动子）。拟议的混合系统计算寡核苷酸统计数据的分布，作为位置权重矩阵，有助于区分启动子与非启动子。这项研究可以帮助定位DNA的表达区域，预测并实现从DNA的编码区域开始合成的蛋白质的特性，结构和功能。使用留一法对基准数据集进行了评估。实验结果表明，该系统具有较高的精度。

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