在非线性混合效应模型选择和优化中,pyDarwin机器学习算法的应用和比较
Xinnong Li1, Mark Sale2, Keith Nieforth2
1Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences State University of New York at Buffalo, Buffalo, NY, USA.
Journal of pharmacokinetics and pharmacodynamics
|June 28, 2024
概括
五个机器学习 (ML) 算法被测试为前向加法/后向消除 (FABE) 的替代方案,用于人群药理动力学 (PPK) 模型选择. 高斯过程 (GP) 在识别最佳PPK模型方面表现出最高的效率.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 计算生物学 计算生物学
- 机器学习应用 机器学习应用
背景情况:
- 前向加法/后向消除 (FABE) 一直是人口药理动力学 (PPK) 模型选择的传统方法.
- 在PPK中越来越需要更高效和更强大的模型选择技术.
研究的目的:
- 评估五种机器学习 (ML) 算法作为FABE的替代方案,用于PPK模型选择.
- 将ML算法与本地下坡搜索策略相结合的效率和稳定性进行比较.
主要方法:
- 研究了基因算法 (GA),高斯过程 (GP),随机森林 (RF),梯度增强随机树 (GBRT) 和粒子优化 (PSO).
- 结合ML算法与一位或两位本地下坡搜索进行系统的特征探索.
- 使用对1,572,864款车型的详尽搜索作为强度的黄金标准.
主要成果:
- 所有的ML算法,当与二位本地搜索配对时,成功识别了最佳的PPK模型.
- GA,RF,GBRT和GP只使用一位本地搜索确定了最佳模型.
- 高斯过程 (GP) 是最有效的,检查了495个模型,而粒子优化 (PSO) 是最不有效的 (1710个模型). GP需要最长的计算时间 (2975.6分钟),而GA是最快的 (321.8分钟).
结论:
- 机器学习算法,特别是高斯过程,为PPK模型选择提供了高效和强大的FABE替代方案.
- 选择ML算法和本地搜索策略显著影响PPK模型构建中的效率和计算时间.
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