为了寻找Tandem质谱数据集中的脱而出
Krzysztof Jan Abram1,2, Douglas McCloskey1,3
1Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark.
Biomolecules
|September 28, 2023
概括
使用变化自编码器的生成建模可以将联质谱数据解成有意义的表示. 这种方法有助于从MS/MS频谱中创建仪器无关代谢物识别.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习是机器学习.
背景情况:
- 双重质谱 (MS/MS) 对于代谢物识别至关重要.
- 目前的MS/MS数据分析面临的挑战是仪器变化和数据库查询.
- 开发可解释和无助于仪器的表征对于推进代谢学至关重要.
研究的目的:
- 将生成建模和表示学习应用于MS/MS频谱.
- 调查MS/MS光谱的解到潜在的产生因素.
- 创建可解释和仪器无关的代谢物的数字表示.
主要方法:
- 利用变量自编码器 (VAE) 进行生成建模和表示学习.
- 将VAE应用于并联质谱数据,对生成因子的预先知识是最小的.
- 开发了一种两步的方法来选择分离的VAE模型.
主要成果:
- 证明VAE可以有效地解开MS/MS频谱数据.
- 确定了与已知的变化因子 (例如碰撞能量,电离模式) 保持一致的有意义的潜伏表示.
- 展示了仪器无关的数据分析和改进的代谢物识别的潜力.
结论:
- 使用VAE的生成建模为分析MS/MS光谱提供了一种强大的方法.
- 不纠的表示方便了对仪器无关的比较和数据库查询.
- 开发的方法可以扩展到其他高维和复杂的数据集.
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