title: Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.
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.
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States of America.|Laboratory of Fluorescence Dynamics, University of California, Irvine, Irvine, CA, United States of America.|Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, Irvine, CA, United States of America.
authors.forename: Christopher
authors.initials: C
authors.lastname: Heylman
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States of America.|Laboratory of Fluorescence Dynamics, University of California, Irvine, Irvine, CA, United States of America.
authors.forename: Rupsa
authors.initials: R
authors.lastname: Datta
authors.affiliation: Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States of America.
authors.forename: Agua
authors.initials: A
authors.lastname: Sobrino
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States of America.|Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, Irvine, CA, United States of America.|Department of Biomedical Engineering, Washington University in St. Louis. St. Louis, MO, United States of America.
authors.forename: Steven
authors.initials: S
authors.lastname: George
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States of America.|Laboratory of Fluorescence Dynamics, University of California, Irvine, Irvine, CA, United States of America.
authors.forename: Enrico
authors.initials: E
authors.lastname: Gratton
mesh_terms: D000200:Action Potentials / Q000187:drug effects; D000318:Adrenergic beta-Agonists / Q000494:pharmacology*; D000465:Algorithms; D002454:Cell Differentiation / Q000187:drug effects; D002460:Cell Line; D022062:Electrophysiologic Techniques, Cardiac; D006339:Heart Rate / Q000187:drug effects*; D006801:Humans; D057026:Induced Pluripotent Stem Cells / Q000166:cytology / Q000187:drug effects*; D007545:Isoproterenol / Q000494:pharmacology; D032383:Myocytes, Cardiac / Q000187:drug effects*; D011433:Propranolol / Q000494:pharmacology; D000069553:Supervised Machine Learning
chemical_list: D000318:Adrenergic beta-Agonists; D011433:Propranolol; D007545:Isoproterenol