深度学习直接从序列预测高场不对称波形离子运动 (FAIMS) 中的非正常传输分布
Justin McKetney1,2,3,4,5, Ian J Miller1,2, Alexandre Hutton6,7,8
1Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Analytical chemistry
|January 27, 2025
概括
现在可以使用高场不对称波形离子移动 (FAIMS) 预测离子移动性. 机器学习模型,包括长期短期记忆 (LSTM) 网络,在质谱学中准确预测的行为,有助于蛋白质组学研究.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 离子流动性分析增强了基于质谱的蛋白质组学.
- 准确预测离子流动性有助于测试开发和数据库搜索.
- 漂移管离子流动性的预测方法存在,但高场不对称波形离子流动性 (FAIMS) 预测的探索较少.
研究的目的:
- 在FAIMS中开发和验证用于预测离子流动性的模型.
- 探索机器学习方法来建模FAIMS的移动性.
- 为了提高蛋白质组学数据分析的准确性和效率.
主要方法:
- 利用多标签分类方案来模拟离子的移动性.
- 在超过10万个人类前体上训练了一个随机森林和一个长期短期记忆 (LSTM) 神经网络.
- 从两种模型的预测组合起来,以提高性能.
主要成果:
- 整体模型的表现优于单个模型,获得更高的F2分数.
- 在近4万个E.E.的测试集上,以F2分数为0.66和AUROC为0.928的预测性表现. 大肠杆菌离子.离子.
- 确定了模型犯错误的具体情况,为进一步改进提供了洞察力.
结论:
- 在FAIMS中使用机器学习成功模拟了离子运动.
- 开发的深度学习模型为蛋白质组学研究提供了有价值的工具.
- 该模型可以在线访问,促进在测试开发和数据分析中的更广泛应用.
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