mlsanityData to 14 Oct 2026

CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

arXiv 2309.07178 ↗First posted 12 Sep 2023q-bio.QM cs.AI cs.LGcited by 1

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Abstract (arXiv)

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.html

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 69 references that are in the map. First posted on arXiv 12 Sep 2023.

Builds on

6 of 69 references.

References · 6

  1. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
  2. 2022Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonancecited by 22
  3. 2020Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopycited by 17
  4. 2019Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learningcited by 16
  5. 2020A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopycited by 12
  6. 2020Exponential Signal Reconstruction with Deep Hankel Matrix Factorizationcited by 8

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