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Updated: Aug 3, 2026

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在湿和干实验室毒理学研究之间的桥梁上航行,以应对高维数据的当前挑战

Alexis Payton1,2, Kyle R Roell1,3, Meghan E Rebuli2,4,5

  • 1Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Frontiers in toxicology
|June 12, 2023
PubMed
概括

高通量毒理学研究产生复杂,高维数据. 这种观点解决了湿实验室研究人员在数据分析方面的挑战,提出解决方案,并突出了改善培训和合作的持续需求.

关键词:
计算毒理学计算毒理学数据分析数据分析数据分析数据归算数据的归算方法高维数据的高维数据.机器学习是机器学习.

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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...