ArticleAnnals of biomedical engineering2021
Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.
Article in Annals of biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 47 citations in OpenAlex.
- Advanced in vitro cardiac models for drug evaluation: integration of organoids, engineered tissues, and microphysiological systems.Microsystems & nanoengineering · 2026Review
- A machine learning platform for genotype-specific cardiotoxicity risk prediction using patient-derived iPSC-CMs.Journal of advanced research · 2026Article
- Heterogeneity and optimal study design between cell lines in induced pluripotent stem cell-based cardiac disease modeling.Stem cells translational medicine · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Chondrogenic Differentiation of Stem Cells for Cartilage Regeneration: Advances and Future Perspectives.Tissue engineering and regenerative medicine · 2026Review
- Immune and Immune-Integrated Organoids as NextGeneration Platforms for Disease Modeling.MedComm · 2025Review
- Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.Annual review of pharmacology and toxicology · 2025Review
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Regenerative rehabilitation: a novel multidisciplinary field to maximize patient outcomes.Medical review (2021) · 2024Review
- The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity.International journal of molecular sciences · 2024Review
- 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
- Article
- Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development.Frontiers in cell and developmental biology · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Stem cell therapy for heart failure in the clinics: new perspectives in the era of precision medicine and artificial intelligence.Frontiers in physiology · 2023Review
- Emerging role of artificial intelligence in cardiac electrophysiology.Cardiovascular digital health journal · 2022Review
- Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.Journal of tissue engineering and regenerative medicine · 2022Article
- Bioengineering Strategies to Create 3D Cardiac Constructs from Human Induced Pluripotent Stem Cells.Bioengineering (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder. The antiarrhythmic effect of dantrolene, an inhibitor of sarcoplasmic calcium release, was studied in iPSC-CMs after adrenaline, an adrenergic agonist, stimulation by machine learning analysis of calcium transient signals. First, beats of transient signals were identified with our peak recognition algorithm previously developed. Then 12 peak variables were computed for every identified peak of a signal and by means of this data signals were classified into different classes corresponding to those affected by adrenaline or, thereafter, affected by a drug, dantrolene. The best classification accuracy was approximately 79% indicating that machine learning methods can be utilized in analysis of iPSC-CM drug effects. In the future, data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.
Indexed as
Identifiers
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.