Reference - Detail
| RRC ID | 63770 |
|---|---|
| Author | Aghasafari P, Yang PC, Kernik DC, Sakamoto K, Kanda Y, Kurokawa J, Vorobyov I, Clancy CE. |
| Title | A deep learning algorithm to translate and classify cardiac electrophysiology. |
| Journal | Elife |
| Abstract |
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. |
| Volume | 10 |
| Published | 2021-7-2 |
| DOI | 10.7554/eLife.68335 |
| PII | 68335 |
| PMID | 34212860 |
| PMC | PMC8282335 |
| MeSH | Action Potentials / physiology Algorithms* Cell Differentiation / physiology Computer Simulation Deep Learning* ERG1 Potassium Channel / genetics ERG1 Potassium Channel / metabolism Electrophysiologic Techniques, Cardiac* Electrophysiological Phenomena / physiology Gene Expression Regulation / drug effects Humans Induced Pluripotent Stem Cells / physiology Models, Biological Myocytes, Cardiac / physiology* Phenethylamines / pharmacology Sulfonamides / pharmacology |
| IF | 7.08 |
| Altmetric score |
オルトメトリクス指標項目
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| The most frequently cited source | X(Twitter) |
| Total number of mentions | 8 |
| Altmetric score changes over past 6months | 0.0 |
| Resource | |
| Human and Animal Cells | 201B7(HPS0063) |