Artificial neural networks for prediction of mycobacterial promoter sequences
Artificial neural networks for prediction of mycobacterial promoter sequences
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DOI：
10.1016/j.compbiolchem.2003.09.004
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发表时间：
2003-12-01
影响因子：
3.1
通讯作者：
Kulkarni, BD
中科院分区：
文献类型：
--
作者：
Kalate, RN;Tambe, SS;Kulkarni, BD
一个多层前馈人工神经网络结构训练使用误差反向传播（EBP）算法已被开发用于预测是否一个给定的核苷酸序列是分枝杆菌启动子序列。由于开发的网络模型的高预测能力（一致性至97%），它已被进一步用于结合卡尺随机化（CR）的方法，以确定在启动子序列中的结构/功能上重要的区域。由此获得的结果表明：（i）-35框的上游区域、（ii）-35区域、（iii）间隔区和（iv）-10框对于分枝杆菌启动子是重要的。CR方法还表明，-38至-29区域在确定给定序列是否是分支杆菌启动子方面发挥着重要作用。从本质上讲，本研究建立人工神经网络作为一种工具，用于预测分枝杆菌启动子序列，并确定结构/功能上重要的子区域。(C)2003 Elsevier Ltd.保留所有权利。
A multilayered feed-forward ANN architecture trained using the error-back-propagation (EBP) algorithm has been developed for predicting whether a given nucleotide sequence is a mycobacterial promoter sequence. Owing to the high prediction capability (congruent to97%) of the developed network model, it has been further used in conjunction with the caliper randomization (CR) approach for determining the structurally/functionally important regions in the promoter sequences. The results obtained thereby indicate that: (i) upstream region of -35 box, (ii) -35 region, (iii) spacer region and, (iv) -10 box, are important for mycobacterial promoters. The CR approach also suggests that the -38 to -29 region plays a significant role in determining whether a given sequence is a mycobacterial promoter. In essence, the present study establishes ANNs as a tool for predicting mycobacterial promoter sequences and determining structurally/functionally important sub-regions therein. (C) 2003 Elsevier Ltd. All rights reserved.