• Corpus ID: 236428138

Identifying the fragment structure of the organic compounds by deeply learning the original NMR data

  title={Identifying the fragment structure of the organic compounds by deeply learning the original NMR data},
  author={Chongcan Li and Yong Cong and Weihua Deng},
We preprocess the raw NMR spectrum and extract key characteristic features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition; meanwhile may provide the alternative strategy to address the imbalance issue of the NMR dataset frequently encountered in dataset collection of statistical modeling and establish two conventional SVM and KNN models to assess the capability of two feature selection, respectively. Our… 

Figures and Tables from this paper



NMRNet: a deep learning approach to automated peak picking of protein NMR spectra

A convolutional neural network is applied for visual analysis of multidimensional NMR spectra and a combination of extracted peak lists with automated assignment routine, FLYA, outperformed other methods, including the manual one, and led to correct resonance assignment at the levels of 90.40%, 89.90% and 90.20% for three benchmark proteins.

PROSHIFT: Protein chemical shift prediction using artificial neural networks

  • J. Meiler
  • Chemistry
    Journal of biomolecular NMR
  • 2003
A neural network was trained to predict the 1H, 13C, and 15N of proteins using their three-dimensional structure as well as experimental conditions as input parameters, which has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three- dimensional structural proposals.

Deep Learning

Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain

To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy (1H‐MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are

The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning

  • L. Deng
  • Mathematics
  • 2006
Furthermore, if i and j are neighboring locations, then the correlation of observations at those points conditional on all other observations is Qij/ √ QiiQjj , and the conditional mean and precision

A Critical Review of Recurrent Neural Networks for Sequence Learning

The goal of this survey is to provide a selfcontained explication of the state of the art of recurrent neural networks together with a historical perspective and references to primary research.

Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals

Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.

Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements

  • K. Fukushima
  • Computer Science
    IEEE Trans. Syst. Sci. Cybern.
  • 1969
A new type of visual feature extracting network has been synthesized, and the response of the network has be simulated on a digital computer as a first step towards the realization of a recognizer of handwritten characters.

Online Learning and Neural Networks

  • Cambridge University Press
  • 1998