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树林:半监督机器学习集成多个搜索引擎用于标识

Tristan Ranff^1,2,3, Matthew Dennison⁴, Jeroen Bédorf⁴

¹Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany.

Journal of proteome research

|January 22, 2025

概括

树林是一种新的机器学习工具,它结合了多个算法,以获得更好的蛋白质组学中的谱匹配. 这种方法可以提高的识别,同时保持高质量的量化结果.

科学领域:

蛋白质组学是指蛋白质组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

自下而上的蛋白质组学依赖于精确的谱匹配 (PSM).
现有的算法具有不同的优缺点,对用户构成挑战.
需要采用综合方法来提高PSM的准确性和可靠性.

研究的目的:

介绍PeptideForest,这是一个半监督的机器学习方法,用于集成多个PSM算法.
为了增强高质量的与光谱匹配的数量.
通过TMT量化验证频谱分配的质量.

主要方法:

开发了PeptideForest,这是一个随机的森林分类器,其训练来自多个算法的任务.
在Ursgal管道框架中集成PeptideForest.
利用具有已知的基本真相的样本的TMT量化来评估光谱分配质量.

主要成果:

与MS-GF+相比,PeptideForest增加了25.2±1.6%的q值<1%的对光谱匹配,与MS-GF+相比.
通过TMT量化验证,PSM数量的提高并没有影响频谱分配质量.
证明PeptideForest可以更深入地了解自下而上的蛋白质组学数据.

结论:

树有效地集成多个算法,以改善谱匹配在自下而上的蛋白质组学.
该方法提高了的识别,而不会牺牲量化准确性.
树提供了一个有价值的工具,以获得更深入的洞察力蛋白质组学研究.

关键词:

质谱测量质量谱测量机器学习是机器学习.酸标识酸标识的光谱匹配.蛋白质组学蛋白质组学随机的森林随机的森林搜索引擎整合搜索引擎整合目标诱验证目标诱验证

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