通过将多个数据集与染色学参数矢量化和转移学习相结合来预测保留时间.
Yansong Li1, Kunjie Dong1, Di Yu2,3
1School of Computer Science & Technology, Dalian University of Technology, Dalian 116024, China.
Analytical chemistry
|August 1, 2025
概括
这项研究介绍了MDL-TL,这是一种用于预测染色学保留时间 (RTs) 的新型机器学习方法. 通过结合多个数据集和结合色谱参数,MDL-TL可以在各种不同的实验条件下提高预测准确性.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 保留时间 (RT) 对于基于质谱的化合物识别至关重要.
- 由于对实验条件的敏感性和数据稀疏性,RT预测具有挑战性.
- 现有的机器学习模型往往缺乏跨不同染色学系统的概括性.
研究的目的:
- 开发一种强大的机器学习方法,用于准确的保留时间预测.
- 克服数据稀疏性,提高染色体学中的模型通用性.
- 为了在各种不同的实验设置中实现有效的转移学习以预测保留时间.
主要方法:
- 提出了一种多数据集学习与转移学习 (MDL-TL) 的方法.
- 使用word2vec和自动编码器进行矢量色谱参数 (CPs).
- 将CP集成到复合表示中,用于联合多数据集培训和微调.
主要成果:
- MDL-TL显著优于五种深度学习和四种机器学习方法.
- 在28个数据集 (14 RP-LC, 14 HILIC) 中,在平均绝对误差,中位数绝对误差,平均相对误差和R2方面取得了卓越的性能.
- 通过微调,证明了有效的可转移到新的色谱系统.
结论:
- MDL-TL为准确和可概括的保留时间预测提供了一个有前途的解决方案.
- 该方法有效地利用了多数据集学习和转移学习原则.
- 在基于质谱的分析中,MDL-TL提高了化合物识别的可靠性.
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