在MALDI MSI中使用基于神经网络的重新校准改进了质量校准
Alexander Denker1, Jens Behrmann1, Tobias Boskamp2
1Center for Industrial Mathematics, University of Bremen, Bibliothekstraße 5, Bremen, 28359, Germany.
Analytical chemistry
|May 6, 2024
概括
MassShiftNet是一个新的自我监督的学习框架,可以准确地重新校准质谱数据. 这种方法可以提高生物医学研究中的分子映射的质量准确性和对齐性.
科学领域:
- 生物医学研究的研究.
- 分析化学是一种分析化学.
- 计算生物学是一种计算生物学.
背景情况:
- 矩阵辅助激光脱/电离质谱成像 (MALDI MSI) 对于生物样本中的分子映射至关重要.
- 精确的质量校准至关重要,但在MALDI MSI中由于测量过程中的质量转移而具有挑战性.
研究的目的:
- 引入MassShiftNet,这是一个新的自我监督的学习框架,用于MALDI MSI中的大规模重新校准.
- 解决质量转移的挑战,以改善分子识别和绘制.
主要方法:
- 开发了一个自主监督的学习框架,名为MassShift.Net.
- 使用数据依赖和增强训练数据集训练了一个神经网络.
- 应用了框架来重新校准来自MALDI MSI实验的质谱.
主要成果:
- MassShiftNet有效地减少了光谱中的绝对质量误差.
- 该方法增强了谱之间的相对质量对齐.
- 在使用MALDI TOF仪器的FFPE固定组织样本上表现出更好的性能.
结论:
- 在MALDI MSI中,MassShiftNet提供了一个强大的解决方案来进行质量重新校准.
- 该框架提高了生物医学应用中的分子绘图和识别的可靠性.
- 这种方法提升了MALDI MSI用于分析复杂生物样本的实用性.
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