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使用视觉变换器和串联质谱法测定黄酮糖苷异构体

Determination of Flavonoid Glycoside Isomers Using Vision Transformer and Tandem Mass Spectrometry.

作者信息

Park Ji In, Kim Myeong Ji, Lee Kyu Hyeong, Oh Seung Hyun, Kang Young Hoon, Kim Hyunwoo

机构信息

College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University-Seoul, Goyang 10326, Republic of Korea.

出版信息

Plants (Basel). 2024 Dec 4;13(23):3401. doi: 10.3390/plants13233401.

Abstract

A vision transformer (ViT)-based deep neural network was applied to classify the flavonoid glycoside isomers by analyzing electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra. Our model successfully classified the flavonoid isomers with various substitution patterns (3-O, 6-C, 7-O, 8-C, 4'-O) and multiple glycosides, achieving over 80% accuracy during training. In addition, the experimental spectra from flavonoid glycoside standards were acquired with different adducts, and our model showed robust performance regardless of the experimental conditions. As a result, the vision transformer-based computer vision model is promising for analyzing mass spectrometry data.

摘要

一种基于视觉Transformer(ViT)的深度神经网络被应用于通过分析电喷雾电离串联质谱(ESI-MS/MS)光谱来对黄酮糖苷异构体进行分类。我们的模型成功地对具有各种取代模式(3-O、6-C、7-O、8-C、4'-O)和多种糖苷的黄酮异构体进行了分类,在训练过程中准确率超过80%。此外,还获取了来自黄酮糖苷标准品的具有不同加合物的实验光谱,并且我们的模型在无论实验条件如何的情况下都表现出稳健的性能。因此,基于视觉Transformer的计算机视觉模型在分析质谱数据方面具有广阔前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8f2/11644359/53091033190c/plants-13-03401-g001.jpg

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