Detection of Genetic Cardiac Diseases by Ca Transient Profiles Using Machine Learning Methods

Overview

Journal Sci Rep

Specialty Science

Date 2018 Jun 21

PMID 29921843

Citations 22

Authors

Martti Juhola

Henry Joutsijoki

Kirsi Penttinen

Katriina Aalto-Setala

Affiliations

Soon will be listed here.

Abstract

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have revolutionized cardiovascular research. Abnormalities in Ca transients have been evident in many cardiac disease models. We have shown earlier that, by exploiting computational machine learning methods, normal Ca transients corresponding to healthy CMs can be distinguished from diseased CMs with abnormal transients. Here our aim was to study whether it is possible to separate different genetic cardiac diseases (CPVT, LQT, HCM) on the basis of Ca transients using machine learning methods. Classification accuracies of up to 87% were obtained for these three diseases, indicating that Ca transients are disease-specific. By including healthy controls in the classifications, the best classification accuracy obtained was still high: approximately 79%. In conclusion, we demonstrate as the proof of principle that the computational machine learning methodology appears to be a powerful means to accurately categorize iPSC-CMs and could provide effective methods for diagnostic purposes in the future.

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