Considering discrepancy when calibrating a mechanistic electrophysiology model
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Pras Pathmanathan | Chon Lok Lei | Gary R. Mirams | Chris D. Cantwell | Tammo Delhaas | Sanmitra Ghosh | Richard D. Wilkinson | Dominic G. Whittaker | Marina Riabiz | Yasser Aboelkassem | Alexander V. Panfilov | Keith Worden | Kylie A. Beattie | John Walmsley | Charles Houston | Gustavo Montes Novaes | Rodrigo Weber dos Santos | R. W. dos Santos | R. Wilkinson | K. Worden | T. Delhaas | A. Panfilov | P. Pathmanathan | C. Cantwell | Sanmitra Ghosh | Chon Lok Lei | K. Beattie | Y. Aboelkassem | M. Riabiz | J. Walmsley | G. M. Novaes | C. Houston
[1] Nikolaus Hansen,et al. The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.
[2] Michel Crucifix,et al. On the use of simple dynamical systems for climate predictions , 2009, 0906.3625.
[3] John Salvatier,et al. Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..
[4] William W. S. Wei,et al. Time series analysis - univariate and multivariate methods , 1989 .
[5] Pras Pathmanathan,et al. Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? , 2016, Journal of molecular and cellular cardiology.
[6] Michel Crucifix,et al. Bayesian model selection for the glacial–interglacial cycle , 2015, 1511.03467.
[7] Rodrigo Weber Dos Santos,et al. A robust multi-objective optimization framework to capture both cellular and intercellular properties in cardiac cellular model tuning: Analyzing different regions of membrane resistance profile in parameter fitting , 2019, PloS one.
[8] A. Seheult,et al. Pressure Matching for Hydrocarbon Reservoirs: A Case Study in the Use of Bayes Linear Strategies for Large Computer Experiments , 1997 .
[9] Adrian E. Raftery,et al. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .
[10] Gary Gintant,et al. Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. , 2014, American heart journal.
[11] Daniel B. Ennis,et al. Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology , 2014, PloS one.
[12] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[13] Dan Cornford,et al. Quantifying Simulator Discrepancy in Discrete-Time Dynamical Simulators , 2011 .
[14] Pras Pathmanathan,et al. Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology , 2018, Front. Physiol..
[15] D DiFrancesco,et al. Reciprocal role of the inward currents ib, Na and if in controlling and stabilizing pacemaker frequency of rabbit sino-atrial node cells , 1992, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[16] R. Wilkinson. Approximate Bayesian computation (ABC) gives exact results under the assumption of model error , 2008, Statistical applications in genetics and molecular biology.
[17] R MAIRE,et al. [Perspectives of the future]. , 1950, Le Medecin generaliste de France.
[18] A. Stephen McGough,et al. Black-Box Variational Inference for Stochastic Differential Equations , 2018, ICML.
[19] Richard E Chandler,et al. Exploiting strength, discounting weakness: combining information from multiple climate simulators , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[20] Keith Worden,et al. On the confidence bounds of Gaussian process NARX models and their higher-order frequency response functions , 2018 .
[21] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..
[22] David Gavaghan,et al. Probabilistic Inference on Noisy Time Series (PINTS) , 2018, Journal of Open Research Software.
[23] N. Trayanova,et al. Computational models in cardiology , 2018, Nature Reviews Cardiology.
[24] Zoubin Ghahramani,et al. Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.
[25] Edward J. Vigmond,et al. Fitting Membrane Resistance along with Action Potential Shape in Cardiac Myocytes Improves Convergence: Application of a Multi-Objective Parallel Genetic Algorithm , 2014, PloS one.
[26] A. O'Hagan,et al. Bayesian calibration of computer models , 2001 .
[27] D. Noble. A modification of the Hodgkin—Huxley equations applicable to Purkinje fibre action and pacemaker potentials , 1962, The Journal of physiology.
[28] K.H.W.J. ten Tusscher,et al. Comments on 'A model for human ventricular tissue' : reply , 2005 .
[29] Rodrigo Weber dos Santos,et al. Combining Polynomial Chaos Expansions and Genetic Algorithm for the Coupling of Electrophysiological Models , 2019, ICCS.
[30] Cesare Corrado,et al. An audit of uncertainty in multi-scale cardiac electrophysiology models , 2020, Philosophical Transactions of the Royal Society A.
[31] Michael Clerx,et al. Myokit: A simple interface to cardiac cellular electrophysiology. , 2016, Progress in biophysics and molecular biology.
[32] Henggui Zhang,et al. High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment. , 2013, American journal of physiology. Heart and circulatory physiology.
[33] Gary R. Mirams,et al. Uncertainty and variability in computational and mathematical models of cardiac physiology , 2016, The Journal of physiology.
[34] A. Stuart,et al. The Bayesian Approach to Inverse Problems , 2013, 1302.6989.
[35] Trine Krogh-Madsen,et al. Cell-Specific Cardiac Electrophysiology Models , 2015, PLoS Comput. Biol..
[36] Pras Pathmanathan,et al. Ensuring reliability of safety-critical clinical applications of computational cardiac models , 2013, Front. Physiol..
[37] Siem Jan Koopman,et al. Time Series Analysis by State Space Methods , 2001 .
[38] Denis Noble,et al. How the Hodgkin–Huxley equations inspired the Cardiac Physiome Project , 2012, The Journal of physiology.
[39] Pras Pathmanathan,et al. Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models , 2019, Front. Physiol..
[40] Carl E. Rasmussen,et al. Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC , 2013, NIPS.
[41] Jenný Brynjarsdóttir,et al. Learning about physical parameters: the importance of model discrepancy , 2014 .
[42] Eric Kerfoot,et al. Verification of cardiac tissue electrophysiology simulators using an N-version benchmark , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[43] A. V. D. Vaart,et al. Misspecification in infinite-dimensional Bayesian statistics , 2006, math/0607023.
[44] Andrew Phillips,et al. Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems , 2019, ICML.
[45] Hui Yang,et al. Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells , 2016 .
[46] Yanan Fan,et al. Handbook of Approximate Bayesian Computation , 2018 .
[47] Min Wu,et al. Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative , 2018, Clinical pharmacology and therapeutics.
[48] Christopher S Oehmen,et al. Mathematical Model of the Rapidly Activating Delayed Rectifier Potassium Current IKr in Rabbit Sinoatrial Node , 2002, Journal of cardiovascular electrophysiology.
[49] A. Raftery,et al. Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .
[50] Peter Willett,et al. What is a tutorial , 2013 .
[51] Michael Clerx,et al. Reproducible model development in the cardiac electrophysiology Web Lab , 2018, bioRxiv.
[52] Hervé Delingette,et al. Patient-specific Electromechanical Models of the Heart for the Prediction of Pacing Acute Effects in Crt: a Preliminary Clinical Validation , 2022 .
[53] Albert Tarantola,et al. Inverse problem theory - and methods for model parameter estimation , 2004 .
[54] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[55] A. Panfilov,et al. Nonstationary vortexlike reentrant activity as a mechanism of polymorphic ventricular tachycardia in the isolated rabbit heart. , 1995, Circulation.
[56] Pras Pathmanathan,et al. Verification of computational models of cardiac electro‐physiology , 2014, International journal for numerical methods in biomedical engineering.
[57] James Hensman,et al. ABC for Climate: Dealing with Expensive Simulators , 2015, Handbook of Approximate Bayesian Computation.
[58] Chon Lok Lei,et al. Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology , 2017, Front. Physiol..
[59] N. Ravishanker,et al. Bayesian Analysis of ARMA Processes: Complete Sampling Based Inference Under Full Likelihoods , 1996 .
[60] Gary R. Mirams,et al. Calibration of ionic and cellular cardiac electrophysiology models , 2020, Wiley interdisciplinary reviews. Systems biology and medicine.
[61] S. Walker,et al. Bayesian asymptotics with misspecified models , 2013 .
[62] Jamie I. Vandenberg,et al. Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics , 2017, bioRxiv.
[63] Michael Goldstein,et al. Second-Order Exchangeability Analysis for Multimodel Ensembles , 2013 .
[64] J. Hancox,et al. Rapid characterisation of hERG channel kinetics II: temperature dependence , 2019, bioRxiv.
[65] Skipper Seabold,et al. Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.
[66] Gary R. Mirams,et al. Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing , 2012, British journal of pharmacology.
[67] Denis Noble,et al. The Cardiac Physiome: perspectives for the future , 2009, Experimental physiology.
[68] Chon Lok Lei,et al. Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System , 2019, Biophysical journal.
[69] Henggui Zhang,et al. Cardiac cell modelling: observations from the heart of the cardiac physiome project. , 2011, Progress in biophysics and molecular biology.
[70] Gary R. Mirams,et al. Rapid Characterization of hERG Channel Kinetics II: Temperature Dependence , 2019, bioRxiv.
[71] Hervé Delingette,et al. Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia , 2011, Interface Focus.
[72] John K Kruschke,et al. Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[73] Denis Noble,et al. Contributions of HERG K+ current to repolarization of the human ventricular action potential. , 2008, Progress in biophysics and molecular biology.
[74] Adrian E. Raftery,et al. Bayesian Model Averaging: A Tutorial , 2016 .
[75] Mark A. Girolami,et al. Bayesian inference for differential equations , 2008, Theor. Comput. Sci..
[76] M. Plumlee. Bayesian Calibration of Inexact Computer Models , 2017 .
[77] Chon Lok Lei,et al. Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system , 2019, bioRxiv.
[78] Dominic G. Whittaker,et al. Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments , 2020, Philosophical Transactions of the Royal Society A.
[79] Michael Goldstein,et al. Bayesian Forecasting for Complex Systems Using Computer Simulators , 2001 .