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阶段模型驱动的深度学习,用于在高通量NMR基代谢学中进行强大的阶段校正

Chuanwen Zhao¹, Gang Chen^1,2, Caixiang Liu^1,2

¹State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.

The journal of physical chemistry letters

|January 9, 2026

概括

一种新的深度学习方法,阶段模型驱动的剩余注意网络 (PD-RAN),准确地纠正高通量NMR代谢的阶段. 这种强大的技术可确保对大型生物样本数据集进行可靠的光谱分析.

科学领域:

分析化学分析化学
生物化学生物化学
计算生物学计算生物学

背景情况:

高通量核磁共振 (NMR) 光谱对于代谢学至关重要,它使生物样本的有效和非侵入性分析成为可能.
精确的NMR光谱相位校正对于在代谢学工作流程中进行可靠的定量分析至关重要.
目前的相位校正方法在大型自动化代谢学研究中可能缺乏稳定性和效率.

研究的目的:

开发和验证一种新的,强大的,高效的相位校正方法,用于高通量NMR代谢学.
提高复杂生物样本光谱数据处理的准确性和可靠性.
解决自动化,大规模NMR分析中传统相位校正技术的局限性.

主要方法:

介绍阶段模型驱动的剩余注意网络 (PD-RAN),一种混合深度学习方法.
将物理知情模型与剩余注意网络集成在一起,以学习低维相位特征.
将PD-RAN应用于来自各种代谢学样本 (大脑提取物,血,尿液) 的1DNMR光谱.

主要成果:

与各种样本类型的传统方法相比,PD-RAN在相位校正方面表现优越.
该方法实现了高精度和可靠性,产生纯吸收模式光谱.
观察到异常的处理速度,在20毫秒内处理了1000个光谱,突出显示了其适用于高通量应用的适用性.
废弃性研究证实了阶段模型驱动组件的有效性,以及该方法对噪声和基线扭曲等光谱工件的稳定性.

结论:

在NMR代谢数据处理中,PD-RAN提供了显著的进步,提供了准确和高效的相位校正.
基于物理的深度学习方法提高了定量代谢学分析的可靠性.
这种方法非常适合现代高通量NMR代谢学的需求,促进可扩展和一致的生物样本分析.

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