A deep learning algorithm to translate and classify cardiac electrophysiology

eLife

Junko Kurokawa¹  Kazuho Sakamoto¹  Colleen E Clancy²  Pei-Chi Yang²  Parya Aghasafari²  Igor Vorobyov³  Yasunari Kanda⁴  Divya C Kernik⁵ 

[1] Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan;Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States;Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States;Department of Pharmacology, University of California, Davis, Davis, United States;Division of Pharmacology, National Institute of Health Sciences, Kanagawa, Japan;Washington University in St. Louis, St. Louis, United States;

关键词: artificial intelligence;    machine learning;    deep learning;    computational biology;    arrhythmias;    pharmacology;    Human;   

DOI  :  10.7554/eLife.68335

来源: eLife Sciences Publications, Ltd

【 摘 要 】

The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.

【 授权许可】

CC BY