On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes. (October 2021)

• Record Type: Journal Article
• Authors: Juhola, Martti; Joutsijoki, Henry; Penttinen, Kirsi; Shah, Disheet; Aalto-Setälä, Katriina
• Is Part Of: Computer methods and programs in biomedicine. Volume 210(2021)
• Publication Date: 2021-10
• Subjects: Genetic cardiac diseases -- Induced pluripotent stem cells -- Cardiomyocytes -- Transient profiles -- Machine learning -- Classification -- Leave-one-out -- K-fold cross-validation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106367
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Six cardiac disease classes and controls were classified with machine learning. Among numerous methods random forests gave the highest classification accuracy. We compared leave-one-out and 10-fold crossvalidation as methods and their results. Abstract: Background: Cardiomyocytes differentiated from human induced pluripotent stem cells (iPSC-CMs) can be used to study genetic cardiac diseases. In patients these diseases are manifested e.g. with impaired contractility and fatal cardiac arrhythmias, and both of these can be due to abnormal calcium transients in cardiomyocytes. Here we classify different genetic cardiac diseases using Ca 2+ transient data and different machine learning algorithms. Methods: By studying calcium cycling of disease-specific iPSC-CMs and by using calcium transients measured from these cells it is possible to classify diseases from each other and also from healthy controls by applying machine learning computation on the basis of peak attributes detected from calcium transient signals. Results: In the current research we extend our previous study having Ca-transient data from four different genetic diseases by adding data from two additional diseases (dilated cardiomyopathy and long QT Syndrome 2). We also study, in the light of the current data, possible differences and relations when machine learning modelling and classification accuracies were computed by using either leave-one-out test or 10-fold cross-validation. Conclusions: Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results.