On computation of calcium cycling anomalies in cardiomyocytes data
On computation of calcium cycling anomalies in cardiomyocytes data
复制标题
心肌细胞数据中钙循环异常的计算
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
K. Aalto
中科院分区：
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作者：
M. Juhola;H. Joutsijoki;Kirsi Varpa;Jyri Saarikoski;J. Rasku;Kati Iltanen;J. Laurikkala;Heikki Hyyrö;Jorge Avalos;H. Siirtola;K. Penttinen;K. Aalto
从心脏病患者皮肤成纤维细胞中诱导的多能干细胞(IPSC)被诱导分化为心肌细胞，并用于生成包含136条记录的钙瞬变数据集。其目的是将正常信号从异常信号中分离出来，供以后的医学研究使用。我们构建了一个信号分析程序来检测信号中代表钙循环的峰，并构建了另一个程序来将它们分类为正常峰或异常峰。使用机器学习方法，根据信号峰值的发现，将信号分为正常信号和异常信号。我们将获得的分类结果与一位专家生物技术专家的视觉分类结果进行了比较，后者独立于计算机方法评估了信号。约85%的分类准确率表明两种模式之间的高度一致性，表明对当前数据进行基于计算机的处理的能力和实用性很高。
Induced pluripotent stem cell (iPSC) lines derived from skin fibroblasts of patients suffering from cardiac disorders were differentiated to cardiomyocytes and used to generate a data set of Ca2+ transients of 136 recordings. The objective was to separate normal signals for later medical research from abnormal signals. We constructed a signal analysis procedure to detect peaks representing calcium cycling in signals and another procedure to classify them into either normal or abnormal peaks. Using machine learning methods we classified signals into normal or abnormal signals on the basis of peak findings in them. We compared classification results obtained to those made visually by an expert biotechnologist who assessed the signals independent of the computer method. Classification accuracies of around 85% indicated high congruence between two modes denoting the high capability and usefulness of computer based processing for the present data.