论文标题
NMR频谱重建作为模式识别问题
NMR spectrum reconstruction as a pattern recognition problem
论文作者
论文摘要
提出了一个基于WaveNet架构（WNN）的新的深神经网络，该网络旨在掌握NMR光谱中的特定模式。当按固定的不均匀抽样（NUS）时间表进行培训时，WNN受益于每个光谱峰会产生的相应点传播功能（PSF）模式的模式识别，从而导致NUS光谱的最高质量和强大的重建在模拟中所证明的，并且在2D 1H-15N透明型号的2d 1H-15N透明镜头中所证明的是8. KDA），Azurin（14 kDa）和Malt1（44 kDa）。在2D甲基1H-13 HMQC的MALT1频谱中，WNN的模式识别也被证明是成功的虚拟均可耦合。我们证明，使用WNN，可以使用有关NUS计划的先验知识（到目前为止尚未完全利用），可用于设计超过现有算法方法的新的强大NMR处理技术。
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of the corresponding point spread function (PSF) pattern produced by each spectral peak resulting in the highest quality and robust reconstruction of the NUS spectra as demonstrated in simulations and exemplified in this work on 2D 1H-15N correlation spectra of three representative globular proteins with different sizes: Ubiquitin (8.6 kDa), Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also demonstrated for successful virtual homo-decoupling in a 2D methyl 1H-13 HMQC spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS schedule, which so far was not fully exploited, can be used for designing new powerful NMR processing techniques that surpass the existing algorithmic methods.
