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Updated: Jul 12, 2025

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机器学习有潜力解决人类毒性和生态毒性表征的数据缺口

Kerstin von Borries1, Hanna Holmquist2, Marissa Kosnik1

  • 1Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark.

Environmental science & technology
|November 1, 2023
PubMed
概括

机器学习 (ML) 可以帮助填补化学毒性评估的数据缺口. 这项研究优先考虑了关键的毒性参数,显示ML模型可以预测8-46%的化学品的数据,即使现有的测量有限.

关键词:
化学性质 化学性质化学空间 化学空间化学替代 化学替代是一种化学替代.生命周期影响评估生命周期影响评估确定优先级,确定优先级.风险查风险查通过设计,安全和可持续.不确定性是一种不确定性.

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