为了优化基于神经网络的NMR代谢量化NMR代谢量化
Hayden Johnson1, Aaryani Tipirneni-Sajja1,2
1Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA.
Metabolites
|April 25, 2025
概括
变压器擅长从核磁共振 (NMR) 光谱中量化代谢物,为代谢学提供快速,自动化的解决方案. 这种先进的深度学习方法提高了准确性,特别是对于复杂的样本.
科学领域:
- 计算化学是一种计算化学.
- 生物化学 生物化学
- 机器学习 机器学习
背景情况:
- 从NMR光谱中精确,高吞吐量量化代谢物对代谢学至关重要.
- 神经网络在定量NMR代谢学中未得到充分利用,尽管它们具有速度和吞吐量的潜力.
- 传统的峰值配合软件对于复杂的光谱来说可能很慢,效率也很低.
研究的目的:
- 通过直接从模拟的NMR光谱中使用神经网络调查数据集和模型开发以量化代谢物.
- 为了比较多层感知子,卷积神经网络和转换器模型对NMR代谢学的性能.
- 优化模型架构,训练参数和数据集,以准确量化代谢物.
主要方法:
- 使用模拟的400MHz 1H-NMR光谱测试了含有8,44或86种代谢物的混合物.
- 三个神经网络模型 (MLP,CNN,变压器) 被训练并优化.
- 模型在100MHz和800MHz模拟的光谱上进行了验证.
主要成果:
- 变压器模型在NMR代谢物量化方面表现出卓越的性能.
- 变压器在增加代谢物数量,低度或大动态范围时最有效.
- 通过广泛的光谱范围 (100-MHz至800-MHz) 实现了精确的量化.
结论:
- 变压器显示出在NMR代谢学中准确,实时,完全自动化代谢物量化的巨大潜力.
- 进一步开发实验数据可能会导致先进的自动化定量NMR代谢学软件.
- 这种方法为复杂的光谱分析提供了传统方法的有希望的替代方案.
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