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fickleheart-method-tutorials

Code for the tutorials in the Fickle Heart model calibration with discrepancy 2020 paper.

https://github.com/cardiacmodelling/fickleheart-method-tutorials

Science Score: 13.0%

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    Found 5 DOI reference(s) in README
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    Low similarity (9.4%) to scientific vocabulary

Keywords

autoregressive-moving-average bayesian-inference calibration electrophysiology gaussian-processes model-discrepancy uncertainty-quantification
Last synced: 6 months ago · JSON representation

Repository

Code for the tutorials in the Fickle Heart model calibration with discrepancy 2020 paper.

Basic Info
  • Host: GitHub
  • Owner: CardiacModelling
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 830 MB
Statistics
  • Stars: 3
  • Watchers: 3
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
autoregressive-moving-average bayesian-inference calibration electrophysiology gaussian-processes model-discrepancy uncertainty-quantification
Created over 6 years ago · Last pushed about 3 years ago
Metadata Files
Readme Citation

README.md

Model calibration with discrepancy

This repo contains the code for reproducing the results in the examples in the paper "Considering discrepancy when calibrating a mechanistic electrophysiology model" by Lei, Ghosh, Whittaker, Aboelkassem, Beattie, Cantwell, Delhaas, Houston, Novaes, Panfilov, Pathmanathan, Riabiz, dos Santos, Walmsley, Worden, Mirams, and Wilkinson. doi:10.1098/rsta.2019.0349.

Requirements

The code requires Python (3.5+) and the following dependencies: PINTS, Myokit, Theano, StatsModels, Joblib.

In addition, Myokit requires a working compiler (e.g. gcc) and Sundials (CVODE) to be installed. For instructions, see http://myokit.org/install.

To setup, either run (for Linux/macOS users): console $ bash setup.sh or navigate to the path where you downloaded this repo and run: $ pip install --upgrade pip $ pip install .

Action potential model example

See action-potential-models.

Ion channel model example

See ion-channel-models.

Acknowledging this work

If you publish any work based on the contents of this repository please cite (CITATION file):

Lei, C.L. et al. (2020). Considering discrepancy when calibrating a mechanistic electrophysiology model. Philosophical Transactions of the Royal Society A, 378: 20190349.

Owner

  • Name: Cardiac Modelling
  • Login: CardiacModelling
  • Kind: organization
  • Location: United Kingdom

Codes and Resources from the University of Nottingham's cardiac modelling team

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