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Critical Assessment of Parameter Estimation Methods in Models of Biological Oscillators ?

机译:生物振荡器模型中参数估计方法的关键评估

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Many biological systems exhibit oscillations in relation to key physiological or cellular functions, such as circadian rhythms, mitosis and DNA synthesis. Mathematical modelling provides a powerful approach to analysing these biosystems. Applying parameter estimation methods to calibrate these models can prove a very challenging task in practice, due to the presence of local solutions, lack of identifiability, and risk of overfitting. This paper presents a comparison of three state-of-the-art methods: frequentist, Bayesian and set-membership estimation. We use the Fitzhugh-Nagumo model with synthetic data as a case study. The computational performance and robustness of these methods is discussed, with a particular focus on their predictive capability using cross-validation.
机译:许多生物系统表现出与关键生理或细胞功能有关的振荡,例如昼夜节律,有丝分裂和DNA合成。数学建模为分析这些生物系统提供了强大的方法。由于存在局部解决方案,缺乏可识别性和过度拟合的风险,因此应用参数估计方法来校准这些模型在实践中可能会证明是一项非常具有挑战性的任务。本文介绍了三种最新方法的比较:常客,贝叶斯和集合成员估计。我们使用带有综合数据的Fitzhugh-Nagumo模型作为案例研究。讨论了这些方法的计算性能和鲁棒性,特别着重于使用交叉验证的预测能力。

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