用多层系统生物学推进基于人工智能的毒性预测:关于基因毒性的案例研究
Xin Zhang1,2, Huazhou Zhang1,2, Xiao Yun1,3
1State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 100085, P. R. China.
Briefings in bioinformatics
|November 16, 2025
概括
GenotoxNet是一个新的深度学习框架,集成化学结构和生物数据来预测化学基因毒性. 这种多模式的方法提高了准确性,并有助于对危险化学品的风险评估.
科学领域:
- 计算毒理学计算毒理学
- 基因组学就是基因组学.
- 机器学习是机器学习.
背景情况:
- 化学多样性对健康和环境风险评估构成挑战.
- 毒性预测受到异质细胞对化学物质暴露的反应的阻碍.
- 整合多式联运数据对于预测个体健康影响至关重要.
研究的目的:
- 开发一个多式联络深度学习框架,GenotoxNet,以提高基因毒性预测.
- 系统地整合化学结构,体外测定数据和转录组学数据.
- 通过捕捉细胞异质性和机械复杂性来改善化学诱导的基因毒性的预测.
主要方法:
- GenotoxNet框架使用多式联络深度学习.
- 整合化学结构,高通量体外测定数据和转录组学数据.
- 在内部和外部测试集上使用AUCROC评估模型性能.
主要成果:
- 在内部测试组中,GenotoxNet的AUCROC为0.891±0.017.
- 多式联运方法的表现优于单式联运预测模型.
- 该模型在外部化学装置上表现出强的性能.
结论:
- 基因毒素网为预测基因毒性提供了一个强大的框架.
- 该模型通过将特征与不良结果途径 (AOPs) 对齐来促进机制性解释.
- 这种方法支持对危险化学品的预防战略和监管决策.
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