Atrial cell action potential parameter fitting using genetic algorithms.
about
Exploiting mathematical models to illuminate electrophysiological variability between individualsInter-subject variability in human atrial action potential in sinus rhythm versus chronic atrial fibrillation.Fitting membrane resistance along with action potential shape in cardiac myocytes improves convergence: application of a multi-objective parallel genetic algorithmCell-specific cardiac electrophysiology models.Comparison of Detailed and Simplified Models of Human Atrial Myocytes to Recapitulate Patient Specific Properties.Modeling an Excitable Biosynthetic Tissue with Inherent Variability for Paired Computational-Experimental StudiesRapid genetic algorithm optimization of a mouse computational model: benefits for anthropomorphization of neonatal mouse cardiomyocytesParameter Estimation of Ion Current Formulations Requires Hybrid Optimization Approach to Be Both Accurate and ReliableVariability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm.Optimisation of a generic ionic model of cardiac myocyte electrical activity.Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology.Dynamics of human atrial cell models: restitution, memory, and intracellular calcium dynamics in single cells.Optimisation of ionic models to fit tissue action potentials: application to 3D atrial modelling.Nonlinear dynamics in cardiology.Properties of two human atrial cell models in tissue: restitution, memory, propagation, and reentry.Anthropomorphizing the mouse cardiac action potential via a novel dynamic clamp method.Computational modeling of the human atrial anatomy and electrophysiology.Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms.A Genetic Algorithm Optimization Method for Mapping Non-Conducting Atrial Regions: A Theoretical Feasibility Study.Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes.Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology.Efficient parameterization of cardiac action potential models using a genetic algorithm.Modelling variability in cardiac electrophysiology: a moment-matching approach.Differential roles of two delayed rectifier potassium currents in regulation of ventricular action potential duration and arrhythmia susceptibility.A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research
P2860
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P2860
Atrial cell action potential parameter fitting using genetic algorithms.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年学术文章
@wuu
2005年学术文章
@zh
2005年学术文章
@zh-cn
2005年学术文章
@zh-hans
2005年学术文章
@zh-my
2005年学术文章
@zh-sg
2005年學術文章
@yue
2005年學術文章
@zh-hant
name
Atrial cell action potential parameter fitting using genetic algorithms.
@en
Atrial cell action potential parameter fitting using genetic algorithms.
@nl
type
label
Atrial cell action potential parameter fitting using genetic algorithms.
@en
Atrial cell action potential parameter fitting using genetic algorithms.
@nl
prefLabel
Atrial cell action potential parameter fitting using genetic algorithms.
@en
Atrial cell action potential parameter fitting using genetic algorithms.
@nl
P2093
P356
P1476
Atrial cell action potential parameter fitting using genetic algorithms.
@en
P2093
P2860
P304
P356
10.1007/BF02351029
P577
2005-09-01T00:00:00Z