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首页> 外文期刊>Journal of computational biology >Strong/Weak Feature Recognition of Promoters Based on Position Weight Matrix and Ensemble Set-Valued Models
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Strong/Weak Feature Recognition of Promoters Based on Position Weight Matrix and Ensemble Set-Valued Models

机译:基于位置权重矩阵和集合集值模型的启动子强弱特征识别

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摘要

In this article, we propose a method to recognize the strong/weak property of the promoters based on the nucleotide sequence. To the best of our knowledge, it is the first time to predict the strong/weak property of the promoters. First, position weight matrix (PWM) is used to evaluate the contributions of the nucleotides to the promoter strength. Then, the set-valued model is used to describe the relation between the nucleotide sequence and the strength. Considering the small-sample and imbalance features of the promoter data, we propose an ensemble approach to predict the strong/weak property of the promoters. The proposed method is used to recognize 60promoters ofEscherichia coli. The results show the effectiveness of the proposed method. This article provides a simple way for a biologist to evaluate the strong/weak feature of promoters from the nucleotide sequence.
机译:在本文中,我们提出了一种基于核苷酸序列识别启动子强弱特性的方法。据我们所知,这是第一次预测启动子的强/弱特性。首先,位置权重矩阵(PWM)用于评估核苷酸对启动子强度的贡献。然后,使用设定值模型描述核苷酸序列和强度之间的关系。考虑到启动子数据的小样本和不平衡特征,我们提出了一种集成方法来预测启动子的强/弱属性。所提出的方法用于识别大肠杆菌的60个启动子。结果表明了该方法的有效性。本文为生物学家提供了一种从核苷酸序列评估启动子强弱特性的简单方法。

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