峰值侦探:一种基于深度学习的半监督方法,用于在非目标代谢学中达到峰值治愈
Ethan Stancliffe1,2,3, Gary J Patti1,2,3,4
1Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Analytical chemistry
|June 14, 2023
概括
PeakDetective是一款新的深度学习工具,可以准确地区分真正的代谢物与非目标代谢学数据中的工件. 这种自动化方法显著改善了研究人员的数据质量和下游分析.
科学领域:
- 计算生物学 计算生物学
- 分析化学 分析化学
- 生物化学 生物化学
背景情况:
- 未定位的代谢学产生了大量文物的大型数据集,使分析复杂化.
- 当前的峰值检测算法优先考虑灵敏性而不是选择性,导致高错误阳性率.
- 现有的文物移除方法需要大量的手动干预,因为不同的峰形状.
研究的目的:
- 开发一种自动化,准确的方法来将峰值分类为真实代谢产物或代谢学数据中的工件.
- 为了减少数据处理中由丰富文物峰值列表引起的瓶.
- 提高非目标代谢学数据分析的可靠性和效率.
主要方法:
- 开发了PeakDetective,这是一个半监督的深度学习方法,用于峰值分类.
- 使用无监督的自动编码器将特征提取到隐藏空间.
- 使用主动学习来快速进行分类器培训,使用最小的用户标记数据.
主要成果:
- 与现有方法相比,PeakDetective在五个不同的LC/MS数据集中在删除文物方面表现出卓越的准确性.
- 积极学习方法需要最小的用户输入 (少于100个峰值) 快速模型培训.
- 对SARS-CoV-2数据集的应用确定了更多具有统计意义的代谢物,增强了生物见解.
结论:
- PeakDetective提供了一个高度准确和高效的解决方案,用于处理非目标代谢学数据.
- 该工具可以快速适应特定的分析方法和样本类型.
- PeakDetective 改进了代谢物识别,使生物发现更加稳健,并促进了传染病等领域的研究.
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