Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy

DOI10.3934/mbe.2019176zbMath1497.92136OpenAlexW2939859915WikidataQ93200240 ScholiaQ93200240MaRDI QIDQ2160715

Yang Kuang, Eric J. Kostelich, Tin Phan, Javier Baez, Zhimin Wu

Publication date: 3 August 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2019176

zbMATH Keywords

parameter estimationmathematical modelingprostate cancerensemble Kalman filteruncertainty quantificationparameter identifiabilityandrogen suppression

Mathematics Subject Classification ID

Medical applications (general) (92C50) Mathematical modeling or simulation for problems pertaining to biology (92-10)

Related Items (6)

Modeling the synergistic properties of drugs in hormonal treatment for prostate cancer ⋮ Modeling the effect of immunotherapies on human castration-resistant prostate cancer ⋮ Rich dynamics of a simple delay host-pathogen model of cell-to-cell infection for plant virus ⋮ Predicting the effectiveness of chemotherapy using stochastic ODE models of tumor growth ⋮ A study of disproportionately affected populations by race/ethnicity during the SARS-CoV-2 pandemic using multi-population SEIR modeling and ensemble data assimilation ⋮ Dynamics of a non-smooth model of prostate cancer with intermittent androgen deprivation therapy

Cites Work

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• On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics

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