ArticleStem cell reports2017
Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.
Article in Stem cell reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electrical stimulation through conductive scaffolds for cardiomyocyte tissue engineering: Systematic review and narrative synthesis.Annals of the New York Academy of Sciences · 2022Pooled it
- A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.Advanced intelligent discovery · 2026Article
- Electromechanical modelling and simulation of human-induced pluripotent stem cell-derived cardiomyocytes predict drug-induced contractility effects.The Journal of physiology · 2026Article
- A Deep Learning-Based Scoring Framework for Large-Scale Multi-Donor Cardiotoxicity Screening.bioRxiv : the preprint server for biology · 2026Article
- A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.bioRxiv : the preprint server for biology · 2026Article
- 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
- Smarter stem cells: how AI is supercharging iPSC technology.Cell and tissue research · 2025Review
- Novel 2D/3D Hybrid Organoid System for High-Throughput Drug Screening in iPSC Cardiomyocytes.Therapeutics · 2025Article
- Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI.Computational and structural biotechnology journal · 2025Article
- Three-dimensional cardiac models: a pre-clinical testing platform.Biochemical Society transactions · 2024Review
- Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.Cell reports methods · 2024Article
- Advances in cardiac tissue engineering and heart-on-a-chip.Journal of biomedical materials research. Part A · 2024Review
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- Noninvasive analysis of contractility during identical maturations revealed two phenotypes in ventricular but not in atrial iPSC-CM.American journal of physiology. Heart and circulatory physiology · 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
- Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients.Computers in biology and medicine · 2023Article
- Organs-on-a-chip: a union of tissue engineering and microfabrication.Trends in biotechnology · 2023Review
- Rianú: Multi-tissue tracking software for increased throughput of engineered cardiac tissue screening.Computer methods and programs in biomedicine update · 2023Article
- Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.Journal of tissue engineering and regenerative medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug.
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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.