Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Author Info

Christopher Heylman, University of California, IrvineFollow
Rupsa Datta, University of California, Irvine
Agua Sobrino, University of California, Irvine
Steven George, Washington University in St. Louis
Enrico Gratton, University of California, Irvine

Recommended Citation

Published in PLOS ONE, December 1, 2015.

The definitive version is available at https://doi.org/10.1371/journal.pone.0144572.

Abstract

Supervised machine learning can be used to predict which drugs human cardiomyocytes have been exposed to. Using electrophysiological data collected from human cardiomyocytes with known exposure to different drugs, a supervised machine learning algorithm can be trained to recognize and classify cells that have been exposed to an unknown drug. Furthermore, the learning algorithm provides information on the relative contribution of each data parameter to the overall classification. Probabilities and confidence in the accuracy of each classification may also be determined by the algorithm. In this study, the electrophysiological effects of β–adrenergic drugs, propranolol and isoproterenol, on cardiomyocytes derived from human induced pluripotent stem cells (hiPS-CM) were assessed. The electrophysiological data were collected using high temporal resolution 2-photon microscopy of voltage sensitive dyes as a reporter of membrane voltage. The results demonstrate the ability of our algorithm to accurately assess, classify, and predict hiPS-CM membrane depolarization following exposure to chronotropic drugs.

Disciplines

Biomedical Engineering and Bioengineering

Copyright

© 2015 Heylman et al

Number of Pages

15