ArticlePloS one2015
Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 37 citations in OpenAlex.
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Using induced pluripotent stem cells for drug discovery in arrhythmias.Expert opinion on drug discovery · 2024Review
- Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.Cell reports methods · 2024Article
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.IEEE open journal of engineering in medicine and biology · 2024Article
- Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.Journal of tissue engineering and regenerative medicine · 2022Article
- Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning.Stem cell reviews and reports · 2022Review
- Intersection of stem cell biology and engineering towards next generationFrontiers in bioengineering and biotechnology · 2022Review
- Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile.ACS biomaterials science & engineering · 2021Article
- Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.Annals of biomedical engineering · 2021Article
- How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab.Journal of the American Society for Mass Spectrometry · 2020Article
- Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning.Analytical chemistry · 2019Article
- The application of convolutional neural network to stem cell biology.Inflammation and regeneration · 2019Review
- Development of a Human iPSC Cardiomyocyte-Based Scoring System for Cardiac Hazard Identification in Early Drug Safety De-risking.Stem cell reports · 2018Article
- Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning.Analytica chimica acta · 2018Article
- Detection of genetic cardiac diseases by CaScientific reports · 2018Article
- Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.Stem cell reports · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Supervised machine learning can be used to predict which drugs human cardiomyocytes have been exposed to. Using electrophysiological data collected from human cardiomyocytes with known exposure to different drugs, a supervised machine learning algorithm can be trained to recognize and classify cells that have been exposed to an unknown drug. Furthermore, the learning algorithm provides information on the relative contribution of each data parameter to the overall classification. Probabilities and confidence in the accuracy of each classification may also be determined by the algorithm. In this study, the electrophysiological effects of β-adrenergic drugs, propranolol and isoproterenol, on cardiomyocytes derived from human induced pluripotent stem cells (hiPS-CM) were assessed. The electrophysiological data were collected using high temporal resolution 2-photon microscopy of voltage sensitive dyes as a reporter of membrane voltage. The results demonstrate the ability of our algorithm to accurately assess, classify, and predict hiPS-CM membrane depolarization following exposure to chronotropic drugs.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.