引入"识别概率"用于自动化和可转移的评估代谢物识别信心在代谢学和相关研究的代谢学和相关研究
Thomas O Metz1, Christine H Chang1, Vasuk Gautam2
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Analytical chemistry
|December 19, 2024
概括
使用识别概率,一种新的自动化和可转移的方法,可以提高代谢学化合物识别的信心. 这种方法通过计算1/N来量化模两可,其中N是分子数据库匹配的数量.
科学领域:
- 代谢学 代谢学 代谢学
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 已建立的化合物识别对代谢学信心的方法,如代谢物标准倡议 (MSI) 水平,已经发展,但面临局限性.
- 当前的定性和定量系统不能完全解决模两可的问题,也不能促进自动化和平台间的可转移性.
研究的目的:
- 提出和评估识别概率作为一种新的方法,用于自动化和可转移的评估化合物识别信心和含糊性在代谢学.
- 为科学界内部的识别概率的透明实施提供指导.
主要方法:
- 定义识别概率为1/N,其中N表示测量分子在指定精度内的数据库匹配次数.
- 使用从人类新陈代谢数据库和计算性属性预测中获得的多属性参考库进行了in silico分析.
主要成果:
- 在对化合物识别模糊性的 in silico 评估中证明了识别概率的实用性.
- 拟议的方法在定义的化学空间内提供了可量化的模糊度量.
结论:
- 识别概率为评估化合物识别信心和代谢学中的模糊性提供了一个有希望的,自动化的和可转移的解决方案.
- 鼓励代谢学界评估鉴定概率与现有的信任评估方法一起.
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