条件通用微分方程捕捉了种群动态和c-产量的个体间变化
Max de Rooij1,2, Natal A W van Riel3,4, Shauna D O'Donovan3,4
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. m.d.rooij@tue.nl.
NPJ systems biology and applications
|July 31, 2025
概括
条件通用微分方程 (cUDEs) 解决了生物医学系统生物学中的数据异质性. 这种新方法通过引入个体特定的参数,增强机器学习模型,改善了对不同患者群体的概括性.
科学领域:
- 生物医学系统生物学 生物学
- 机器学习 机器学习
- 计算生理学计算生理学
背景情况:
- 通用微分方程 (UDEs) 将机械模型与机器学习集成为数据驱动的发现.
- 现有的UDE培训方法与数据异质性和跨人群的概括性作斗争.
- 过度装配是数据驱动的UDE模型面临的重大挑战.
研究的目的:
- 开发一种新的条件UDE (cUDE) 框架,以适应个人之间的数据变化.
- 在异质人群中提高UDE模型的通用性.
- 应用cUDEs来模拟c-的产生,并得出分析表达式.
主要方法:
- 提出了一个有条件的UDE (cUDE) 架构,具有共享的网络结构/权重和个人特定的调节参数.
- 在不同的数据集上训练cUDE模型,代表正常的葡萄糖耐受性,受损的葡萄糖耐受性和2型糖尿病.
- 利用符号回归来从训练的cUDE模型中推导出一个可概括的分析表达式.
主要成果:
- 在不同的葡萄糖耐受性群体中,cUDE模型准确地描述了食后c-水平.
- 条件参数有效地捕捉了c-产生的相关个体间变化.
- 符号回归成功产生了c-动态的可概括分析表达式.
结论:
- 条件UDE为UDE框架提供了强大的扩展,用于处理生物医学建模中的数据异质性.
- 在复杂的生物系统中,cUDE方法提高了模型的概括性和解释性.
- 这项工作为系统生物学中的数据驱动发现提供了新的途径,改善了患者分层.
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