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Updated: Feb 28, 2026

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以代谢学驱动的数据增强机器学习用于预测微塑料混合物的毒性

Beilei Yuan1, Chengzhi Liu2, Shuang Chen1

  • 1College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China.

Ecotoxicology and environmental safety
|February 25, 2026
PubMed
概括

由于复杂的混合物,预测微塑料 (MP) 毒性具有挑战性. 一个由代谢学驱动的机器学习模型有效地预测了MP细胞毒性,为细胞能量代谢重编程提供了洞察力.

关键词:
细胞毒性 细胞毒性数据增强数据增强机器学习 机器学习微塑料是一种微塑料.在QSAR中使用QSAR.

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Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...
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