Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8473799 | Journal of Molecular and Cellular Cardiology | 2016 | 14 Pages |
Abstract
We present two case studies in which probability distributions, instead of individual numbers, are inferred from data to describe quantities such as maximal current densities. Then we show how these probabilistic representations of model parameters enable probabilities to be placed on predicted behaviours. We demonstrate how changes in these probability distributions across data sets offer insight into which currents cause beat-to-beat variability in canine APs. We conclude with a discussion of the challenges that this approach entails, and how it provides opportunities to improve our understanding of electrophysiology.
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Authors
Ross H. Johnstone, Eugene T.Y. Chang, RĂ©mi Bardenet, Teun P. de Boer, David J. Gavaghan, Pras Pathmanathan, Richard H. Clayton, Gary R. Mirams,