Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood

By A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling, U. Klingmüller, Jens Timmer. Bioinform., 2009

1,364 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/610da0cbfd950d525d40a5b6ea8cd4c2fd96aa82

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In Control and Systems Engineering

In Molecular Biology

Citations in the corpus

Statistical inference for dynamical systems: A review (2012, background): “Thus, several recent works have investigated methods for parameter estimation (possibly of sets of parameters) in nonidentifiable settings [22, 97, 113, 143, 142, 154, 165].”
Effective connectivity: Influence, causality and biophysical modeling (2011, positive): “This is the basic idea behind the profile likelihood approach (Raue et al., 2009), which is based on the profile of the evidence for models with different parameter values.”
Bayesian Solution Uncertainty Quantification for Differential Equations (2013, methodology): “Analysis is based on experimental data (Swameye et al., 2003; Raue et al., 2009).”
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology (2013, methodology): “For investigation of confidence intervals and identifiability of parameters the profile likelihood approach can be used [26].”
High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2 (2020, methodology): “The likelihood-based confidence intervals was computed by methods reported in Raue et al (2).”
Time-domain fitting of battery electrochemical impedance models (2015, methodology): “Practical identifiability analysis [40] is an approach to assess the data.”
Decomposition trajectories of diverse litter types: a model selection analysis (2014, methodology): “This is again a consequence of the formulation and parameterization of these models: in the case of the discrete parallel, discrete series and continuous exponential models, the parameters to be fitted are practically (if not structurally) non-identifiable (sensu Raue et al. 2009).”
Systems biology informed deep learning for inferring parameters and hidden dynamics (2019, methodology): “Usually, experimental data are insufficient considering the size of the model, which results in parameters that are non-identifiable [15] or only identifiable within confidence intervals (see more details in [16]).”
Daily precipitation concentration around the world according to several indices (2016, methodology): “Statistical analysis The nor index was obtained using the profile log-likelihood (PLL) approach (Raue et al., 2009).”