人工智能-亚里士多德:系统生物学灰盒识别的物理信息框架
Nazanin Ahmadi Daryakenari1, Mario De Florio2, Khemraj Shukla2
1Center for Biomedical Engineering, School of Engineering, Brown University, Providence, Rhode Island, United States of America.
PLoS computational biology
|March 12, 2024
概括
我们介绍AI-亚里士多德,这是一个新的基于物理的框架,用于发现系统生物学中的治理方程. 这种方法集成了先进的机器学习技术,用于准确的参数估计和识别复杂生物系统中未知的物理.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 从数据中发现生物系统的数学方程是一个关键的科学挑战.
- 现有的方法经常与参数估计和识别未知的物理定律 (灰色框识别) 斗争.
研究的目的:
- 介绍AI-亚里士多德,一个新的基于物理的框架,用于参数估计和灰色盒子识别系统生物学.
- 通过使用基准问题来评估框架的准确性,速度,灵活性和稳定性.
主要方法:
- 人工智能-亚里士多德结合了功能连接的极端理论 (X-TFC) 和物理信息的神经网络 (PINNs) 与符号回归 (SR).
- 该框架利用域分解,并将神经网络与符号回归器集成在一起.
- 使用稀疏的合成数据和添加噪声来评估性能.
主要成果:
- 在药物动力学和葡萄糖-胰岛素模型上,AI-Aristotle在参数估计和灰色框识别方面表现出了准确性和稳定性.
- 在X-TFC和PINNs之间进行了比较,并使用两种不同的SR技术进行了交叉验证.
- 该研究提供了关于整合神经网络和符号回归的性能权衡的见解.
结论:
- 在复杂的动态系统中,AI-亚里士多德为灰色框识别提供了一个全面的方法.
- 该框架为生物医学和其他处理数据驱动科学发现的领域的研究人员提供了宝贵的指导.
- 将神经网络与符号回归集成,提高了从观测数据中发现潜在物理原理的能力.
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