在代谢物识别工作流程中的机器学习辅助假阳性检测
Ramon Adàlia1,2, Paula Cifuentes2,3, Joyce Liu4
1Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193, Spain.
Analytical chemistry
|December 10, 2025
概括
本研究引入了一种机器学习方法,以减少代谢物识别中的错误阳性. 该方法通过分析质谱数据来提高药物发现的准确性和效率.
科学领域:
- 药理学和化学信息学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 代谢物识别对于药物发现和开发至关重要.
- 液体染色学-质谱学 (LC-MS) 数据分析是复杂的,容易产生假阳性.
- 准确的代谢物识别对于了解药物代谢和疗效至关重要.
研究的目的:
- 开发和验证基于机器学习的方法,以提高代谢物识别中错误阳性检测的准确性.
- 提高药物发现工作流程的效率和可靠性.
- 将专家知识与LC-MS数据整合起来,以进行可靠的代谢物表征.
主要方法:
- 使用专家知识开发了与代谢物相关的染色学峰值的特征集.
- 来自质谱,色谱信号和运动谱的综合数据.
- 在各种数据集 (公共和专有) 上使用渐变增强决策树分类器验证了方法.
主要成果:
- 机器学习辅助技术显著减少了假阳性识别.
- 开发的特征集准确地描述了真假阳性和假阳性.
- 该方法在各种数据集上显示出高精度,包括小分子和新模式.
结论:
- 机器学习通过减少错误阳性结果显著提高了代谢物识别的准确性.
- 这种方法提高了药物发现和开发管道的效率和可靠性.
- 多模数据和专家知识的整合是稳健的代谢物识别的关键.
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