在METLIN SMRT中找到可能错误的条目.
Mikhail Khrisanfov1, Dmitriy Matyushin2, Andrey Samokhin1
1A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Moscow, Russia; Chemistry Department, Lomonosov Moscow State University, Moscow, Russia.
Journal of chromatography. A
|February 15, 2025
概括
一种新方法有效地过了METLIN SMRT高性能液态染色学 (HPLC) 数据集中的错误条目. 这种方法提高了机器学习模型和实验用途的数据质量.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 化学信息学 化学信息学
背景情况:
- 梅林SMRT数据集对于高性能液态染色学 (HPLC) 中的保留时间预测至关重要.
- 对于METLIN SMRT,现有的数据过管道往往不足,导致潜在的不准确性.
- 需要一种可靠的方法来识别和删除错误的条目,以提高数据集的质量.
研究的目的:
- 适应并应用一个强大的过方法,以检查METLIN SMRT数据集中可能存在错误的条目.
- 为了提高METLIN SMRT数据集的可靠性,用于机器学习应用和实验研究.
- 评估预测模型在识别大规模染色学数据集中的数据异常方面的有效性.
主要方法:
- 将气色谱保留指数的现有过方法重新应用到METLIN SMRT数据集中.
- 采用了五种预测模型:图形神经网络 (GNN),卷积神经网络 (CNN),扩展连接指纹 (ECFP),功能连接密度 (FCD) 和CatBoost.
- 使用5倍交叉验证策略来预测保留时间,并使用"黄卡"系统标记具有显著偏差的条目 (底部5%).
主要成果:
- 大约1500条条目 (数据集的2%) 在五个模型中至少收到了一个"黄色卡".
- 估计有1200个条目被确定为强烈怀疑的错误数据点.
- 大约300条条目被标记为可能不准确的预测,与错误的条目不同.
结论:
- 开发的过方法可用于提高METLIN SMRT数据集的质量.
- 这种方法具有显著的潜力,可以增强其他大规模的染色学相关数据库.
- 提高数据质量既有利于机器学习模型的训练,也有利于直接的实验利用.
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