SMART‐Miner: A convolutional neural network‐based metabolite identification from 1H‐13C HSQC spectra. (30th December 2021)

• Record Type: Journal Article
• Authors: Kim, Hyun Woo; Zhang, Chen; Cottrell, Garrison W.; Gerwick, William H.
• Other Names: Kuhn Stefan guestEditor.
• Is Part Of: Magnetic resonance in chemistry. Volume 60:Number 11(2022)
• Page Start: 1070
• Page End: 1075
• Publication Date: 2021-12-30
• Subjects: NMR Metabolomics -- Deep Learning -- Convolutional Neural Network -- HSQC Spectra -- Structure Identification -- Mixture Analysis; Nuclear magnetic resonance spectroscopy -- Periodicals; Chemistry, Organic -- Periodicals; Magnetic resonance -- Periodicals; 538.36
• DOI: 10.1002/mrc.5240
• Languages: English
• ISSNs: 0749-1581

Abstract

Abstract: 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. Abstract : The accurate identification of individual metabolites in a mixture is challenging. We applied a convolutional neural network (CNN) to 1 H‐ 13 C HSQC NMR spectra to achieve accurate peak identification in complex mixtures.