引入"识别概率",用于在代谢学和相关研究中对代谢物识别信心的自动和可转移的评估
Thomas O Metz1, Christine H Chang1, Vasuk Gautam2
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA.
bioRxiv : the preprint server for biology
|August 12, 2024
概括
在代谢学中评估化合物识别是具有挑战性的. 这项研究提出了自动化,可转移的信任评估的识别概率 (1/N),解决了跨化学空间的模两可.
科学领域:
- 代谢学 代谢学 代谢学
- 计算化学的计算化学
- 分析化学 分析化学
背景情况:
- 化合物识别对代谢学的信心已经被争论了二十年.
- 现有的方法,如代谢物标准倡议 (MSI) 级别和识别点方案,在自动化,可转移性和解决模两可方面存在局限性.
- 目前的方法并不能完全考虑标识的化学空间背景.
研究的目的:
- 提出识别概率作为一种新的,自动化的,可转移的方法来评估化合物识别信心和含糊性在代谢学.
- 为实施识别概率提供明确的定义和框架.
- 鼓励社区对这种新方法以及现有方法进行评估.
主要方法:
- 定义识别概率为1/N,其中N是参考库中在指定测量精度内的匹配化合物的数量.
- 通过使用来自人类新陈代谢数据库和计算预测的多属性参考库进行了in silico分析.
- 为透明实施识别概率概念制定了指导方针.
主要成果:
- 在in silico分析中证明了识别概率的实用性.
- 强调了化合物识别的自动化和可转移评估的潜力.
- 展示了识别概率如何在定义的化学空间内量化模两可.
结论:
- 鉴定概率为在代谢学中对化合物鉴定信心和模糊性的自动化和可转移评估提供了一个有希望的解决方案.
- 这种方法解决了当前定性和定量评分系统的局限性.
- 邀请进一步的社区评估来验证和完善识别概率概念.
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