基于AI和ML的化学品风险评估:预测化学品诱导的基因组不稳定性引起的致癌风险
Ajay Vikram Singh1, Preeti Bhardwaj1, Peter Laux1
1Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Frontiers in toxicology
|December 11, 2024
概括
人工智能 (AI) 和机器学习 (ML) 与omics技术相结合,提高了化学风险评估. 这些工具预测了由克拉斯托根诱导的基因组不稳定性引起的致癌风险,改善了公共卫生和环境安全.
科学领域:
- 环境科学 环境科学
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 化学风险评估对于公共卫生和环境安全至关重要.
- 人工智能 (AI) 和机器学习 (ML),以及omics技术,正在改变化学风险评估.
- 这些进展提供了新的毒性见解,预测模型和风险管理策略.
研究的目的:
- 探索AI/ML和omics在预测致癌风险方面的协同潜力.
- 用集成的AI/ML和omics方法破译克拉斯托根诱导的基因组不稳定性.
- 概述这一跨学科领域的发现,挑战和机遇.
主要方法:
- 审查综合AI/ML和omics研究的关键发现.
- 分析各个部门的成功应用程序和案例研究.
- 阐明化学诱导致癌的基础分子路径.
主要成果:
- 人工智能/ML和omics的整合提供了一个全面的框架来了解化学物质暴露.
- 基因毒性和突变性预测建模得到了增强.
- 可以更有效地阐明致癌的分子机制.
结论:
- 整合AI/ML和omics显著推进化学风险评估和癌症预防.
- 增强的预测能力改善了公共卫生保护,并为监管决策提供了信息.
- 未来的工作应侧重于数据集成,高级机器学习,翻译研究和为更健康的未来实施政策.
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