Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks.
Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks.
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DOI：
10.1186/1471-2105-11-s6-s17
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发表时间：
2010-10-07
影响因子：
3
通讯作者：
Markovets AA
中科院分区：
文献类型：
--
作者：
Bland C;Newsome AS;Markovets AA
生物学中的主要挑战之一是正确识别启动子区域。 基于模体搜索的计算方法一直是传统的方法。 最近的研究表明，DNA的结构特性，如曲率，堆积能，和应力诱导的双链体去稳定化（SIDD）是有用的启动子预测，以及。 在本文中，目前使用的SIDD能量阈值方法相比，建议人工神经网络（ANN）的方法，寻找启动子的SIDD配置文件数据的基础上。与SIDD阈值预测方法相比，人工神经网络在一定范围内的精确度、召回率和F分数方面都有明显的改善。 ANN分类器的最大F-分数为62.3，基于阈值的分类器的最大F-分数为56.8。人工神经网络用于预测启动子基于SIDD概况数据。 使用这种技术的结果比以前的SIDD阈值方法有所改进。 在广泛的精确度-召回值范围内，人工神经网络比基于阈值的方法更能够识别启动子区域的独特特征。
One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data. When compared to the SIDD threshold prediction method, artificial neural networks showed noticeable improvements for precision, recall, and F-score over a range of values. The maximal F-score for the ANN classifier was 62.3 and 56.8 for the threshold-based classifier. Artificial neural networks were used to predict promoters based on SIDD profile data. Results using this technique were an improvement over the previous SIDD threshold approach. Over a wide range of precision-recall values, artificial neural networks were more capable of identifying distinctive characteristics of promoter regions than threshold based methods.