Detection of genetic cardiac diseases by Ca(2+) transient profiles using machine learning methods.
Detection of genetic cardiac diseases by Ca(2+) transient profiles using machine learning methods.
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DOI：
10.1038/s41598-018-27695-5
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发表时间：
2018-06-19
影响因子：
4.6
通讯作者：
Aalto-Setälä K
中科院分区：
文献类型：
--
作者：
Juhola M;Joutsijoki H;Penttinen K;Aalto-Setälä K
人类诱导多能干细胞衍生的心肌细胞（hiPSC-CM）已经彻底改变了心血管研究。在许多心脏病模型中，Ca 2+瞬变的抑制是明显的。我们之前已经表明，通过利用计算机器学习方法，可以将对应于健康CM的正常Ca 2+瞬变与具有异常瞬变的患病CM区分开来。在这里，我们的目的是研究是否有可能使用机器学习方法根据Ca 2+瞬变来区分不同的遗传性心脏病（CPVT，LQT，HCM）。这三种疾病的分类准确率高达87%，表明Ca 2+瞬变是疾病特异性的。通过在分类中包括健康对照，获得的最佳分类准确率仍然很高：约79%。总之，我们证明了计算机器学习方法似乎是准确分类iPSC-CM的强大手段，并可以在未来为诊断目的提供有效的方法。
Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have revolutionized cardiovascular research. Abnormalities in Ca2+ transients have been evident in many cardiac disease models. We have shown earlier that, by exploiting computational machine learning methods, normal Ca2+ 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 Ca2+ transients using machine learning methods. Classification accuracies of up to 87% were obtained for these three diseases, indicating that Ca2+ 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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影响因子：
5.3
作者：
Novak A;Barad L;Lorber A;Gherghiceanu M;Reiter I;Eisen B;Eldor L;Itskovitz-Eldor J;Eldar M;Arad M;Binah O
通讯作者：
Binah O
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Joutsijoki, Henry;Meissner, Kristian;Juhola, Martti
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通讯作者：
Tertoolen, L
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作者：
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通讯作者：
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