Machine learning identifies abnormal Ca²⁺ transients in human induced pluripotent stem cell-derived cardiomyocytes.

ABSTRACT: Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca²⁺ transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca²⁺ transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca²⁺ transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profi

SUBMITTER: Hwang H 

PROVIDER: S-EPMC7550597 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature