改善液体染色学质谱学化合物适应性的预测,以增强非向分析

Nathaniel Charest1, Charles N Lowe2, Christian Ramsland3

  • 1Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA. charest.nathaniel@epa.gov.

概括

这项研究增强了在毒理风险评估中用于非定向分析 (NTA) 的机器学习模型. 改进的模型更好地预测化学物质对质谱学的适应性,有助于污染物识别和风险表征.

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