Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks
Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks
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DOI：
10.1093/bioinformatics/bty581
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发表时间：
2018-12-15
期刊：
影响因子：
5.8
通讯作者：
Fujiwara, Toshimichi
中科院分区：
文献类型：
--
作者：
Kobayashi, Naohiro;Hattori, Yoshikazu;Fujiwara, Toshimichi
动机：多维NMR谱通常用于NMR信号分配和结构分析。有几个程序可以实现高度自动化的NMR信号分配和结构分析。另一方面，NMR光谱往往有大量的噪声峰，即使是良好的样品和机器条件下获得的数据，它仍然是难以消除这些噪声peaks.Results：我们已经开发出一种方法，以消除噪声峰使用卷积神经网络，在程序包Filt_Robot中实现。Filt_Robot应用于2D和3D NMR光谱时，过滤准确度约为90-95%，产生的非噪声峰数量接近相应手动准备峰列表中的数量。该滤波方法可大大提高核磁共振谱的自动分析能力。
Motivation: Multi-dimensional NMR spectra are generally used for NMR signal assignment and structure analysis. There are several programs that can achieve highly automated NMR signal assignments and structure analysis. On the other hand, NMR spectra tend to have a large number of noise peaks even for data acquired with good sample and machine conditions, and it is still difficult to eliminate these noise peaks.Results: We have developed a method to eliminate noise peaks using convolutional neural networks, implemented in the program package Filt_Robot. The filtering accuracy of Filt_Robot was around 90-95% when applied to 2D and 3D NMR spectra, and the numbers of resulting non-noise peaks were close to those in corresponding manually prepared peaks lists. The filtering can strongly enhance automated NMR spectra analysis.