Review and prospect: deep learning in nuclear magnetic resonance spectroscopy
Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective
X Xue, H Sun, M Yang, X Liu, HY Hu, Y Deng… - Analytical …, 2023 - ACS Publications
The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared,
and ultraviolet–visible spectra, is critical for obtaining molecular structural information. The …
and ultraviolet–visible spectra, is critical for obtaining molecular structural information. The …
DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and
unambiguous identification and characterization of peaks is a difficult, but critically important …
unambiguous identification and characterization of peaks is a difficult, but critically important …
[HTML][HTML] Deconvolution of 1D NMR spectra: A deep learning-based approach
The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and
characterize their parameters, often referred to as deconvolution, is a crucial step in the …
characterize their parameters, often referred to as deconvolution, is a crucial step in the …
An effective image representation method using kernel classification
H Wang, J Wang - 2014 IEEE 26th international conference on …, 2014 - ieeexplore.ieee.org
The learning of image representation is always the most important problem in computer
vision community. In this paper, we propose a novel image representation method by …
vision community. In this paper, we propose a novel image representation method by …
NMR in metabolomics: From conventional statistics to machine learning and neural network approaches
NMR measurements combined with chemometrics allow achieving a great amount of
information for the identification of potential biomarkers responsible for a precise metabolic …
information for the identification of potential biomarkers responsible for a precise metabolic …
NMRNet: a deep learning approach to automated peak picking of protein NMR spectra
Motivation Automated selection of signals in protein NMR spectra, known as peak picking,
has been studied for over 20 years, nevertheless existing peak picking methods are still …
has been studied for over 20 years, nevertheless existing peak picking methods are still …
Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra
Rapid progress in machine learning offers new opportunities for the automated analysis of
multidimensional NMR spectra ranging from protein NMR to metabolomics applications …
multidimensional NMR spectra ranging from protein NMR to metabolomics applications …
Preventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach
Organic foods are among the most susceptible to fraud and mislabeling since the
differentiation between organic and conventionally grown food relies on a paper-trail-based …
differentiation between organic and conventionally grown food relies on a paper-trail-based …
Smart: A mapreduce-like framework for in-situ scientific analytics
In-situ analytics has lately been shown to be an effective approach to reduce both I/O and
storage costs for scientific analytics. Develo** an efficient in-situ implementation, however …
storage costs for scientific analytics. Develo** an efficient in-situ implementation, however …