在辐射研究中,人工智能辅助的不良结果途径 (AOP) 的基础
Vinita Chauhan1, Olivier Armant2, Karine Audouze3
1Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada.
International journal of radiation biology
|February 3, 2026
概括
人工智能 (AI) 和机器学习 (ML) 可以通过自动化数据分析和证据集成来加速辐射不利结果途径 (AOP) 的发展. 这种方法简化了理解辐射的过程.
科学领域:
- 辐射保护 辐射保护
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 负面结果途径 (AOP) 对于辐射风险评估至关重要,但由于手动数据集成和复杂性而受到阻碍.
- 不同来源的碎片化数据使得开发强大的AOP变得复杂.
研究的目的:
- 探索人工智能 (AI) 和机器学习 (ML) 在克服辐射挑战中的应用.
- 提出一个分阶段,人工智能驱动的计划,以加快AOP建设和数据整合在辐射保护.
主要方法:
- 使用AI/ML来提取,注释和整合异质数据源.
- 应用自然语言处理来挖掘科学文献以获得机械洞察力.
- 使用监督和无监督的ML来识别关键事件 (KE) 和关键事件关系 (KER).
- 使用知识图和机械定向的概率推理构建因果模型.
主要成果:
- 通过挖掘文献和数据集,AI/ML可以识别KE,推断KER,并建议AOP结构.
- 拟议的人工智能驱动计划有助于自动化叙事生成,证据评分和模型改进.
- 人工智能/ML集成有望加快数据整合和辐射系统组织的发展.
结论:
- AI/ML提供了一种方法论基础,可以更有效地开发辐射AOP.
- 这种方法有助于系统地组织,整合和优先考虑用于辐射保护的生物和实验数据.
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