In the present postgenomic era, the capacity to generate big data has far exceeded the capacity to analyze, contextualize, and make sense of the data in clinical, biological, and ecological applications. There is a great unmet need for automation and algorithms to aid in analyses of big data, in biology in particular. In this context, it is noteworthy that computational methods used to analyze the regulation of bacterial gene expression have in the past focused mainly on Escherichia coli promoters due to the large amount of data available. The challenge and prospects of automation in prediction and recognition of bacteria sequences as promoters have not been properly addressed due to the promoter size and degenerate pattern. We report here an original neural network approach for recognition and prediction of Bacillus subtilis promoters. The artificial neural network used as input 767 B. subtilis promoter sequences, while also aiming at identifying the architecture, provides the most optimal prediction. Two multilayer perceptron neural network architectures offered the highest accuracy: one with five, and another with seven neurons in the hidden layer. Each architecture achieved an accuracy of 98.57% and 97.69%, respectively. The results collectively indicate the promise of the application of neural network approaches to the B. subtilis promoter recognition problem, while also suggesting the broader potential of algorithms for automation of data analyses in the postgenomic era.

中文翻译：

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向后基因组数据分析自动化的算法：枯草芽孢杆菌启动子的人工神经网络预测。

在当前的后基因组时代，生成大数据的能力已经远远超过了在临床，生物学和生态学应用中分析，关联和理解数据的能力。迫切需要自动化和算法来协助大数据分析，尤其是生物学。在这种情况下，值得注意的是，由于可获得的大量数据，过去用于分析细菌基因表达调控的计算方法主要集中在大肠杆菌启动子上。由于启动子的大小和简并模式，尚未正确解决作为启动子的细菌序列的预测和识别中自动化的挑战和前景。我们在这里报告用于识别和预测神经网络的原始神经网络方法枯草芽孢杆菌启动子。用作输入767枯草芽孢杆菌启动子序列的人工神经网络，同时也旨在识别架构，可提供最佳预测。两种多层感知器神经网络架构提供了最高的准确性：一种在隐藏层中具有五个，而另一种在结构中具有七个神经元。每种架构的准确度分别为98.57％和97.69％。结果共同表明了将神经网络方法应用于枯草芽孢杆菌启动子识别问题的希望，同时也暗示了后基因组时代用于数据分析自动化的算法的广阔潜力。