神经网络用于将模拟的NMR光谱从低场转换为高场,用于定量代谢学
Hayden Johnson1, Aaryani Tipirneni-Sajja1,2
1Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA.
Metabolites
|December 27, 2024
概括
一个变压器网络有效地将低场NMR光谱转换为高场,增强了对代谢物量化的数据. 对低场光谱的直接分析显示了可比的准确性,这表明需要进一步研究以获得最佳的定量NMR光谱.
科学领域:
- 核磁共振 (NMR) 光谱学 核磁共振 (NMR) 光谱学
- 计算化学的计算化学
- 代谢学 代谢学 代谢学
背景情况:
- 基板NMR仪器为研究和工业提供可访问和负担得起的NMR光谱.
- 低磁场NMR光谱仪在光谱分辨率和信号噪声比 (SNR) 中存在挑战,使定量分析复杂化.
- 提高低场NMR光谱的质量对于准确的分析物量化至关重要.
研究的目的:
- 评估神经网络架构,用于将低场 (100 MHz) NMR频谱转换为高场 (400 MHz) 频谱.
- 提高光谱质量,以改善代谢物的定量分析.
- 为了比较使用低场和高场转换光谱的直接代谢物量化性能.
主要方法:
- 模拟的100MHz的NMR光谱使用各种神经网络架构进行处理,包括变压器和多层感知子 (MLP).
- 主要任务是将低场光谱转换为高场光谱.
- 此外,MLP还用于从模拟的低场 (100 MHz) 和高场 (400 MHz) 频谱中直接量化代谢物.
主要成果:
- 变压器网络展示了可靠地将低场NMR光谱转换为高场光谱的能力,即使在复杂的混合物 (21和87代谢物) 中也是如此.
- 使用MLP对低场光谱进行直接代谢物量化,比使用转换为高场光谱的光谱获得略高的准确性.
- 这些发现表明,从低场光谱直接量化可能是足够的,尽管需要进一步验证.
结论:
- 基于变压器的方法有效地将低场NMR光谱转换为高场光谱在代谢应用中.
- 这种方法有可能在各种NMR光谱学领域实现数据处理的自动化.
- 需要进一步的研究和实验验证,才能充分确定低场与高场光谱转换在量化方面的好处.
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