Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.
Magn Reson Chem. 2022 Nov;60(11):1070-1075. doi: 10.1002/mrc.5240. Epub 2021 Dec 30.
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 H- 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.
从复杂的生物体液和组织提取物中鉴定代谢物是了解代谢谱的一个必要过程。核磁共振（NMR）光谱广泛应用于代谢组学研究中，用于鉴定和定量代谢物。然而，对于具有较高峰强度或不同代谢物具有相似化学位移的单个代谢物的准确鉴定仍然是一个具有挑战性的过程。在这项研究中，我们将卷积神经网络（CNN）应用于 H- C HSQC NMR 光谱，以实现复杂混合物中准确的峰识别。结果表明，神经网络成功地接受了从这些 2D NMR 光谱中进行代谢物鉴定的训练，并与其他基于 NMR 的代谢组学工具相比表现出非常好的性能。
