代谢物注释信心评分 (MACS):一种新的MSI识别评分工具
Seth M Eisenberg1, Kevan T Knizner1, David C Muddiman1
1FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States.
Journal of the American Society for Mass Spectrometry
|August 22, 2023
概括
与现有的分数相比,一种新的评分系统 - - 代谢物注释信心分数 (MACS) - - 提供了一种更可靠的方法来评估在质谱成像 (MSI) 数据中化合物识别的信心.
科学领域:
- 分析化学 分析化学
- 生物技术是生物技术.
- 分子成像学分子成像学
背景情况:
- 质谱成像 (MSI) 可视化样本中的离子分布.
- 化合物识别依赖于将质量与电荷比率 (m/z) 与数据库相匹配.
- 高分辨率,精确的质量 (HRAM) MSI数据通常使用像METASPACE这样的平台进行注释.
研究的目的:
- 引入和评估代谢物注释信心评分 (MACS) 系统.
- 为了解决HRAM-MSI数据的信任评分方面的局限性.
- 为了将MACS与METASPACE的现有代谢物-信号匹配 (MSM) 得分进行比较.
主要方法:
- 使用关键MSI指标开发了MACS评分系统:质量精度,光谱精度和空间分布.
- 描述了MACS系统的性能.
- 将MACS得分与METASPACE生成的MSM得分进行对比,用于注释的离子.
主要成果:
- 来自METASPACE的MSM得分往往缺乏一致和合理的信心水平.
- MACS为HRAM-MSI数据提供了更强大的注释信心指标.
- 该研究详细介绍了MACS的表征和比较性能.
结论:
- MACS为评估对HRAM-MSI的注释信心提供了一个优质的替代方案.
- 这种新的评分系统提高了MSI中化合物识别的可靠性.
- 在分子成像中,MACS提高了数据分析的可靠性.
关键词:
在 IR-MALDESII 中,在 MATLAB 中,我们可以使用 MATLAB.在SSIM中,SSIM就是SSIM.标注评分 标注评分 标注评分质量测量准确度 质量测量准确度频谱精度 频谱精度 频谱精度更多相关视频
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