Cardiomyocyte Emulator Training Data
DOI10.5281/zenodo.10640339Zenodo10640339MaRDI QIDQ6717422
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 9 February 2024
Copyright license: No records found.
The data presented in this repository is the training data (data set #1) used in our work Neural network emulation of the human ventricular cardiomyocyte action potential: A tool for more efficient computation in pharmacological studies, available at https://doi.org/10.7554/eLife.91911. The data set was generated by computing 40,000 cardiomyocyte simulations using the ToR-ORd ionic model by Tomek et al. (ToR-ORd-dynCl: an update of the ToR-ORd model of human ventricular cardiomyocyte with dynamic intracellular chloride). It contains both the used maximum conductances and corresponding action potentials (APs). Usage The data was created to be easily read into python or matlab based scripts. The file aps.npz and aps.mat contain the two arrays t of shape [T] and aps of shape [N, T]. A python demo script to load the data is given below, and another one for matlab can be found in the README. Python import numpy as np import pandas as pd import matplotlib.pyplot as plt #Loading the data max_conds_df = pd.read_csv("max_conds.csv").drop("db_index", axis=1) #Ignore the db index with np.load("aps.npz") as f: t, aps = f["t"], f["aps"] #Selecting a random AP nr_aps = max_conds_df.shape[0] ap_i = np.random.choice(nr_aps) #Plotting/printing the random AP and its maximum conductances print("Visualizing the simulated AP for the maximum conductances:") print(max_conds_df.iloc[ap_i]) plt.figure() plt.plot(t, aps[ap_i]) plt.show() Citation If you use these data in your research, please cite the publication from which the data originated: Grandits, T., Augustin, C. M., Haase, G., Jost, N., Mirams, G. R., Niederer, S. A., Plank, G., Varr, A., Virg, L., Jung, A. (2023). Neural network emulation of the human ventricular cardiomyocyte action potential: A tool for more efficient computation in pharmacological studies. eLife, 12. https://doi.org/10.7554/eLife.91911 @article{grandits_neural_2023, title = {Neural network emulation of the human ventricular cardiomyocyte action potential: a tool for more efficient computation in pharmacological studies}, volume = {12}, shorttitle = {Neural network emulation of the human ventricular cardiomyocyte action potential}, url = {https://elifesciences.org/reviewed-preprints/91911}, doi = {10.7554/eLife.91911}, language = {en}, journal = {eLife}, author = {Grandits, Thomas and Augustin, Christoph M. and Haase, Gundolf and Jost, Norbert and Mirams, Gary R. and Niederer, Steven A. and Plank, Gernot and Varr, Andrs and Virg, Lszl and Jung, Alexander}, month = dec, year = {2023}}
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