ArticleStem cell reports2022
Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning.
Article in Stem cell reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Deep Learning-Based Scoring Framework for Large-Scale Multi-Donor Cardiotoxicity Screening.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening.Computational and structural biotechnology journal · 2026Article
- Cardiac Tissue Bioprinting: Integrating Structure and Functions Through Biomimetic Design, Bioinks, and Stimulation.Gels (Basel, Switzerland) · 2025Review
- Gut Fungi in Alzheimer's Disease: Mechanisms, Biomarkers and Therapeutic Potential.Aging and disease · 2025Review
- Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing challenges.Nature methods · 2025Review
- Engineered model of heart tissue repair for exploring fibrotic processes and therapeutic interventions.Nature communications · 2024Article
- The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity.International journal of molecular sciences · 2024Review
- Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.Cell biology and toxicology · 2024Article
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use of waveform parameters to predict cardiac activity merit deeper investigation. In this study, we derived, evaluated, and applied 38 waveform parameters in a novel Python framework, including (among others) peak frequency, peak amplitude, peak widths, and a novel parameter, shoulder-tail ratio. We then trained a random forest model to predict cardiac activity based on the 25 parameters selected by correlation analysis. The area under the curve (AUC) obtained for leave-one-compound-out cross-validation was 0.86, thereby replicating the predictions of conventional methods and outperforming fingerprint-based methods by a large margin. This work demonstrates that machine learning is able to automate the assessment of cardiovascular liability from waveform data, reducing any risk of user-to-user variability and bias.
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Registered trials
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