使用视觉变压器和并联质谱测量来确定黄酸糖类异构体
Ji In Park1, Myeong Ji Kim1, Kyu Hyeong Lee1
1College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University-Seoul, Goyang 10326, Republic of Korea.
Plants (Basel, Switzerland)
|December 17, 2024
概括
使用视觉转换器 (ViT) 的深度学习模型从质谱学数据中准确地分类了黄类糖化物异构体. 这种计算机视觉方法对分析复杂的ESI-MS/MS光谱,即使在不同的实验条件下,也很有前途.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 生物化学 生化学
背景情况:
- 在天然产品研究中,鉴定黄类甘氨酸同位素至关重要.
- 电子喷雾电离双重质谱 (ESI-MS/MS) 是一种用于类化合物分析的强大工具.
- 区分具有相似质谱的异构体是一个重要的分析挑战.
研究的目的:
- 开发和评估一个深度神经网络,使用ESI-MS/MS数据来分类黄类糖化物异构体.
- 为了评估模型的准确性和稳定性跨不同的替代模式和糖化状态.
- 探索计算机视觉技术在质谱数据分析中的应用.
主要方法:
- 使用基于视觉变压器 (ViT) 的深度神经网络.
- 该模型被训练在ESI-MS/MS光谱的黄类糖化物异构体.
- 用各种引证和实验条件获得了 Spectra.
主要成果:
- 在训练过程中,ViT模型在分类黄类糖化物异构体方面取得了超过80%的准确性.
- 该模型在多种不同的替代模式 (例如,3-O,6-C,7-O,8-C,4'-O) 和多个糖化物中显示出强大的性能.
- 尽管实验条件和引证的变化,性能仍然保持一致.
结论:
- 基于视觉变压器的深度学习模型对于从ESI-MS/MS光谱中分类黄类糖化物异构体是有效的.
- 这种计算方法为复杂的质谱数据分析提供了一个有希望的解决方案.
- 该模型的稳定性表明其在自然产品表征中的现实应用潜力.
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