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Software GP_emu_UQSA is an open source Python toolkit designed for fitting a Gaussian process (GP) to model inputs and outputs. The GP can then be used for uncertainty and sensitivity analysis. Workshop slides Quantifying uncertainty in multiscale models for biomedical applications (21 April 2017) Journal publications Chang ET, Strong M, Clayton RH. (2015). Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator. PLoS one, 10 (6), pp. e0130252
(DOI)Ashcroft P, Michor F, Galla T. (2015). Stochastic tunneling and metastable states during the somatic evolution of cancer. Genetics, 199 (4), pp. 1213-28
(DOI)Johnstone RH, Chang ET, Bardenet R, de Boer TP, Gavaghan DJ, Pathmanathan P, Clayton RH. Mirams GR. (2016). Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? Journal of Molecular and Cellular Cardiology, pp. 49-62
(DOI)Stewart PS, Jensen OE. (2015). Patterns of recruitment and injury in a heterogeneous airway network model. Journal of the Royal Society, Interface, 12 (111), pp. 20150523
(DOI)Lin YT, Galla T. (2016). Bursting noise in gene expression dynamics: linking microscopic and mesoscopic models. Journal of the Royal Society, Interface, 13 (114), pp. 20150772
(DOI)Xu F, Jensen OE. (2016). Drop spreading with random viscosity. Proceedings of the Royal Society A. Mathematical, Physical, and Engineering Sciences, 472 (2194), pp. 20160270
(DOI)Lin YT, Doering CR. (2016). Gene expression dynamics with stochastic bursts: Construction and exact results for a coarse-grained model. Physical Review. E, 93 (2), pp. 022409
(DOI)Hufton PG, Lin YT, Galla T, McKane AJ. (2016). Intrinsic noise in systems with switching environments. Physical Review. E, 93 (5), pp. 052119
(DOI)Russell MJ, Jensen OE, Galla T. (2016). Stochastic transport in the presence of spatial disorder: Fluctuation-induced corrections to homogenization. Physical Review. E, 94 (4-1), pp. 042121
(DOI)Chang ET, Lin YT, Galla T, Clayton RH, Eatock J. (2016). A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations. PLoS one, 11 (4), pp. e0152349(DOI)Pearce P, Brownbill P, Janáček J, Jirkovská M, Kubínová L, Chernyavsky IL, Jensen OE. (2016). Image-Based Modeling of Blood Flow and Oxygen Transfer in Feto-Placental Capillaries. PLoS one, 11 (10), pp. e0165369(DOI)Mirams GR, Pathmanathan P, Gray RA, Challenor P, Clayton RH. (2016). Uncertainty and variability in computational and mathematical models of cardiac physiology. The Journal of Physiology, 594 (23), pp. 6833-6847
(DOI)Lin YT, Chang ETY, Eatock J, Galla T, Clayton RH. (2017). Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model. Journal of the Royal Society Interface 14, pp 20160698.
(DOI)Conference proceedings Chang ETY, Clayton RH. (2014). Parameter Sensitivity Analysis of a Human Atrial Cell Model using Multivariate Regression. Computing in Cardiology 2014, 41, pp 521-524
(Link to paper)Eatock J, Lin Y, Chang E, Galla T, Clayton R. (2015). Assessing measures of atrial fibrillation clustering via stochastic models of episode recurrence and disease progression. Computing in Cardiology 2014, 42, pp 265-268(Link to paper)Chang E, Clayton R. (2015). Parameter sensitivity from single atrial cell to tissue: How much does it matter? A simulation and multivariate regression study. Computing in Cardiology 2014, 42, pp 441-444(Link to paper)Chang E, Clayton R. (2015). Uncertainty and sensitivity analysis of the Courtemanche-Ramirez-Nattel human atrial cell model using Gaussian Process emulators. Computing in Cardiology 2014, 42, pp 857-860(Link to paper) | ||||

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