SMART-Miner A convolutional neural network-based metabolite identification from 1 H-13 C HSQC spectra

SMART-Miner : A convolutional neural network-based metabolite identification from 1 H-13 C HSQC spectra

© 2021 John Wiley & Sons, Ltd.

Détails bibliographiques
Publié dans:Magnetic resonance in chemistry : MRC. - 1985. - 60(2022), 11 vom: 20. Nov., Seite 1070-1075
Auteur principal: Kim, Hyun Woo (Auteur)
Autres auteurs: Zhang, Chen, Cottrell, Garrison W, Gerwick, William H
Format: Article en ligne
Langue:English
Publié: 2022
Accès à la collection:Magnetic resonance in chemistry : MRC
Sujets:Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural Convolutional Neural Network Deep Learning HSQC Spectra Mixture Analysis NMR Metabolomics Structure Identification Complex Mixtures
Description
Résumé:© 2021 John Wiley & Sons, Ltd.
The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to 1 H-13 C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools
Description:Date Completed 14.10.2022
Date Revised 04.01.2023
published: Print-Electronic
Citation Status MEDLINE
ISSN:1097-458X
DOI:10.1002/mrc.5240