机器学习模型用于预测具有高倾向向于转移到人乳中的异生物化学物质
Sudharsan Vijayaraghavan1, Akshaya Lakshminarayanan2, Naman Bhargava2
1The Institute of Mathematical Sciences (IMSc), Chennai 600113, India.
ACS omega
|March 25, 2024
概括
机器学习模型可以预测环境化学物质转移到母乳中. 一个支持向量机分类器显示出强大的预测性能,有助于婴儿健康风险评估.
科学领域:
- 环境毒理学环境毒理学
- 计算毒理学计算毒理学
- 婴儿健康 婴儿健康
背景情况:
- 环境化学物质对母乳的污染是对婴儿健康的担忧.
- 牛奶与血度 (M/P) 的比率量化化学物质转移到母乳中.
- 预测性毒理学模型可以识别具有高牛奶转移潜力的化学物质.
研究的目的:
- 开发和评估用于预测化学物质转移到母乳中的机器学习模型.
- 为了识别具有高牛奶与血度 (M/P) 比率的化学品.
- 使用大数据集评估预测模型的概括性.
主要方法:
- 使用了375种化学品的精选数据集,这些化学品具有已知的M/P比率.
- 使用多个机器学习算法进行分类和回归.
- 使用内部和外部测试数据集评估模型性能.
主要成果:
- 支持矢量机 (SVM) 分类器在内部测试数据上实现了77.33%的准确性和65.31%的F-score.
- SVM分类器在外部数据上表现出77.78%的敏感性,具有可通用性.
- 回归模型显示了适度的R2值,这表明精确的M/P比率预测存在挑战.
结论:
- 机器学习,特别是SVM分类,显示出预测化学物质转移到母乳中的巨大潜力.
- 准确的M/P比率回归模型仍然具有挑战性,与之前的研究一致.
- 预测性计算毒理学对于评估人类暴露组中的化学物质和保护婴儿健康是有价值的.
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