Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes
Earlier it has been found that peak data of calcium transient signals originating from human induced pluripotent stem cell-derived cardiomyocytes are possible to be used to study how machine learning methods can be applied to separate which cells respond to a drug. Beating behavior of induced plurip...
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Elsevier
2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291482500019X |
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| author | Martti Juhola Henry Joutsijoki Kirsi Penttinen Katriina Aalto-Setälä |
| author_facet | Martti Juhola Henry Joutsijoki Kirsi Penttinen Katriina Aalto-Setälä |
| author_sort | Martti Juhola |
| collection | DOAJ |
| description | Earlier it has been found that peak data of calcium transient signals originating from human induced pluripotent stem cell-derived cardiomyocytes are possible to be used to study how machine learning methods can be applied to separate which cells respond to a drug. Beating behavior of induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) from a symptomatic individual and an asymptomatic individual carrying a mutation for Brugada syndrome was analyzed with Ca2+ imaging method. Using machine learning methods, it is studied whether it is possible to classify the current peak data successfully and whether differences in the two mutant cell lines could be observed. We applied more machine learning methods than before. Baseline signals were first recorded and they were then exposed to adrenaline and these to an antiarrhythmic drug flecainide which should provoke the disease phenotype. Calcium transient signals derived from induced pluripotent stem cell-derived cardiomyocytes were used for all computational analyses executed. Good classification results were generated with effective machine learning methods. Various test situations were applied to study how different parts of data can be separated to ensure their differences. Good results were gained that support the target so that it is possible to analyze whether the drug impacted on iPSC-CMs. It is also possible to separate which cells were affected by the drug and which were not affected. An important finding was that there were significant differences between calcium transient signals data originated from control subjects and patients and also between responses of the cells from symptomatic and asymptomatic individuals. |
| format | Article |
| id | doaj-art-4c6ea100d0874e808660e00f976b5ea5 |
| institution | DOAJ |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-4c6ea100d0874e808660e00f976b5ea52025-08-20T02:54:22ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015410163110.1016/j.imu.2025.101631Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytesMartti Juhola0Henry Joutsijoki1Kirsi Penttinen2Katriina Aalto-Setälä3Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland; Corresponding author.Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandFaculty of Medicine and Health Technology, Tampere University, Tampere, FinlandFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Heart Center, Tampere University Hospital, Tampere, FinlandEarlier it has been found that peak data of calcium transient signals originating from human induced pluripotent stem cell-derived cardiomyocytes are possible to be used to study how machine learning methods can be applied to separate which cells respond to a drug. Beating behavior of induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) from a symptomatic individual and an asymptomatic individual carrying a mutation for Brugada syndrome was analyzed with Ca2+ imaging method. Using machine learning methods, it is studied whether it is possible to classify the current peak data successfully and whether differences in the two mutant cell lines could be observed. We applied more machine learning methods than before. Baseline signals were first recorded and they were then exposed to adrenaline and these to an antiarrhythmic drug flecainide which should provoke the disease phenotype. Calcium transient signals derived from induced pluripotent stem cell-derived cardiomyocytes were used for all computational analyses executed. Good classification results were generated with effective machine learning methods. Various test situations were applied to study how different parts of data can be separated to ensure their differences. Good results were gained that support the target so that it is possible to analyze whether the drug impacted on iPSC-CMs. It is also possible to separate which cells were affected by the drug and which were not affected. An important finding was that there were significant differences between calcium transient signals data originated from control subjects and patients and also between responses of the cells from symptomatic and asymptomatic individuals.http://www.sciencedirect.com/science/article/pii/S235291482500019XCardiac diseasesDrugsHuman induced pluripotent stem cell-derived cardiomyocytesMachine learning methodsClassification |
| spellingShingle | Martti Juhola Henry Joutsijoki Kirsi Penttinen Katriina Aalto-Setälä Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes Informatics in Medicine Unlocked Cardiac diseases Drugs Human induced pluripotent stem cell-derived cardiomyocytes Machine learning methods Classification |
| title | Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes |
| title_full | Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes |
| title_fullStr | Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes |
| title_full_unstemmed | Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes |
| title_short | Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes |
| title_sort | machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using ipsc derived cardiomyocytes |
| topic | Cardiac diseases Drugs Human induced pluripotent stem cell-derived cardiomyocytes Machine learning methods Classification |
| url | http://www.sciencedirect.com/science/article/pii/S235291482500019X |
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