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人工智能:是Exposome月球飞船的阿波罗指导计算机
Fenna C M Sillé1, Thomas Hartung1,2,3
1Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States.
Frontiers in artificial intelligence
|September 26, 2025
概括
人工智能 (AI) 和微生理系统 (MPS) 可以为个性化的健康创造数字双胞胎. 这种方法加速了毒理学测试,朝着疾病预防和监管科学迈进.
科学领域:
- 环境健康科学 环境健康科学
- 毒理学 毒理学 毒理学
- 计算生物学是一种计算生物学.
背景情况:
- 暴露体,包括所有终身环境暴露,在理解和预防人类疾病方面提出了重大挑战.
- 目前的方法难以将复杂的Exposome数据转化为健康和监管科学的可操作见解.
- 整合先进技术对于破译环境因素对人类健康的影响至关重要.
研究的目的:
- 提出一个统一的框架,将微生理系统 (MPS) 和人工智能 (AI) 整合到Exposome研究中.
- 为了使器官,个体和人口的数字双胞胎使用高质量,特定环境的数据.
- 为人类健康和监管科学推进以预防为导向,个性化的范式.
主要方法:
- 利用微生理系统 (MPS) 和多omics平台来生成高可靠性,特定环境的数据.
- 使用人工智能 (AI),特别是深度学习,来分析和解释复杂的Exposome数据流.
- 以MPS和多omics数据的实验反来代校准AI模型.
主要成果:
- 证明深度学习模型可以在特定毒理终点上优于传统的动物试验.
- 展示了在毒理学评估中降低成本和决策时间的潜力.
- 建立了一个用于预测健康建模的"数字双胞胎"的框架.
结论:
- 整合MPS,多omics和AI提供了一种强大的方法来理解Exposome及其对人类健康的影响.
- 这个"Exposome Moonshot"倡议可以彻底改变毒理学测试和监管科学.
- 未来的工作需要扩大模型的适用性,确保数据安全,并优先考虑算法透明度,以便广泛采用.
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