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Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Xiaobo Qu,Yihui Huang,Hengfa Lu,Tianyu Qiu,Di Guo,Tatiana Agback,Vladislav Yu. Orekhov,Zhong Chen +7 more
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TLDR
It is shown that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for large volume of realistic training data usually required in the deep learning approach.Abstract:
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.read more
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Deep metabolome: Applications of deep learning in metabolomics.
Yotsawat Pomyen,Kwanjeera Wanichthanarak,Patcha Poungsombat,Johannes F. Fahrmann,Dmitry Grapov,Sakda Khoomrung +5 more
TL;DR: In this article, deep learning has been successfully applied to various omics data, however, the applications of deep learning in metabolomics are still relatively low compared to others omics.
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Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra
TL;DR: It is shown here that simple deep neural networks can be trained to reconstruct sparsely sampled NMR spectra using a deep neural network and performs as well, if not better than, the currently and widely used techniques.
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Fast time-resolved NMR with non-uniform sampling
TL;DR: This review summarizes efforts to alleviate the problem of limited applicability of multidimensional NMR in time-resolved studies by focusing on techniques based on sparse or non-uniform sampling (NUS), which lead to experimental time reduction by omitting a significant part of the data during measurement and reconstructing it mathematically, adopting certain assumptions about the spectrum.
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In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale.
TL;DR: This review of in-cell structural biology by NMR spectroscopy is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.
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FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.
TL;DR: FID-Net is introduced, a deep neural network architecture inspired by WaveNet that can efficiently virtually decouple 13Cα-13Cβ couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated.
References
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
TL;DR: A framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process is proposed and it is demonstrated that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches.
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Accelerated NMR Spectroscopy by Using Compressed Sensing
Journal ArticleDOI
Fast Multidimensional NMR Spectroscopy Using Compressed Sensing
TL;DR: The use of compressed sensing (CS) 13] as an alternative reconstruction technique for multidimensional NMR spectroscopy is described and it is shown that CS reconstruction compares favorably with ME, and a large reduction in experiment time is possible through the use of CS.