当前位置: X-MOL 学术Ann. Biomed. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.
Annals of Biomedical Engineering ( IF 3.0 ) Pub Date : 2020-05-04 , DOI: 10.1007/s10439-020-02521-0
Martti Juhola 1 , Kirsi Penttinen 2 , Henry Joutsijoki 1 , Katriina Aalto-Setälä 2, 3
Affiliation  

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder. The antiarrhythmic effect of dantrolene, an inhibitor of sarcoplasmic calcium release, was studied in iPSC-CMs after adrenaline, an adrenergic agonist, stimulation by machine learning analysis of calcium transient signals. First, beats of transient signals were identified with our peak recognition algorithm previously developed. Then 12 peak variables were computed for every identified peak of a signal and by means of this data signals were classified into different classes corresponding to those affected by adrenaline or, thereafter, affected by a drug, dantrolene. The best classification accuracy was approximately 79% indicating that machine learning methods can be utilized in analysis of iPSC-CM drug effects. In the future, data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.

中文翻译:

使用机器学习分析药物对 iPSC 心肌细胞的影响。

患者特异性诱导多能干细胞衍生的心肌细胞 (iPSC-CM) 为研究心脏病和治疗结果提供了一个有吸引力的实验平台。在这项研究中,iPSC-CM 被用来通过机器学习(人工智能的核心部分)研究它们的钙瞬态信号和药物作用。在六个携带不同突变的 iPSC 系中评估了药物作用,这些突变导致儿茶酚胺能多形性室性心动过速 (CPVT),这是一种高度恶性的遗传性心律失常疾病。在肾上腺素(一种肾上腺素能激动剂)通过钙瞬态信号的机器学习分析刺激后,在 iPSC-CM 中研究了肌浆钙释放抑制剂丹曲林的抗心律失常作用。第一的,瞬态信号的节拍是用我们之前开发的峰值识别算法来识别的。然后为每个识别出的信号峰值计算 12 个峰值变量,并通过这些数据将信号分为不同的类别,对应于受肾上腺素影响或随后受药物丹曲林影响的那些。最佳分类准确率约为 79%,表明机器学习方法可用于分析 iPSC-CM 药物作用。未来,iPSC-CM 药物作用的数据分析与机器学习方法相结合,除了药物筛选和心脏毒性研究之外,还可以创建一个非常有价值和有效的平台来个性化用药。然后为每个识别出的信号峰值计算 12 个峰值变量,并通过这些数据将信号分为不同的类别,对应于受肾上腺素影响或随后受药物丹曲林影响的那些。最佳分类准确率约为 79%,表明机器学习方法可用于分析 iPSC-CM 药物作用。未来,iPSC-CM 药物作用的数据分析与机器学习方法相结合,除了药物筛选和心脏毒性研究之外,还可以创建一个非常有价值和有效的平台来个性化用药。然后为每个识别出的信号峰值计算 12 个峰值变量,并通过这些数据将信号分为不同的类别,对应于受肾上腺素影响或随后受药物丹曲林影响的那些。最佳分类准确率约为 79%,表明机器学习方法可用于分析 iPSC-CM 药物作用。未来,iPSC-CM 药物效应的数据分析与机器学习方法相结合,除了药物筛选和心脏毒性研究之外,还可以创建一个非常有价值和有效的平台来个性化用药。最佳分类准确率约为 79%,表明机器学习方法可用于分析 iPSC-CM 药物作用。未来,iPSC-CM 药物作用的数据分析与机器学习方法相结合,除了药物筛选和心脏毒性研究之外,还可以创建一个非常有价值和有效的平台来个性化用药。最佳分类准确率约为 79%,表明机器学习方法可用于分析 iPSC-CM 药物效应。未来,iPSC-CM 药物作用的数据分析与机器学习方法相结合,除了药物筛选和心脏毒性研究之外,还可以创建一个非常有价值和有效的平台来个性化用药。
更新日期:2020-05-04
down
wechat
bug