ArticleScientific reports2023
Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- RNA-Binding Protein Signature in Proliferative Cardiomyocytes: A Cross-Species Meta-Analysis from Mouse, Pig, and Human Transcriptomic Profiling Data.Biomolecules · 2025Pooled it
- Chromatin structural gene expression stratifies cardiac cell populations in health and disease.Epigenetics · 2025Article
- Mydgf Enhances Cardiac Angiogenesis by Upregulating FGF1.Journal of cardiovascular translational research · 2025Article
- Newborn apical resection preserves the proliferative capacity of cardiomyocytes located throughout the left ventricle.Stem cells (Dayton, Ohio) · 2025Article
- Follistatin From hiPSC-Cardiomyocytes Promotes Myocyte Proliferation in Pigs With Postinfarction LV Remodeling.Circulation research · 2025Article
- Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research.Molecular biotechnology · 2024Review
- Cell-Cycle-Specific Autoencoding Improves Cluster Analysis of Cycling Cardiomyocytes.Stem cells (Dayton, Ohio) · 2024Article
- Promoting cardiomyocyte proliferation for myocardial regeneration in large mammals.Journal of molecular and cellular cardiology · 2024Review
- Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights.Scientific reports · 2024Article
- Single-cell RNA sequencing analysis identifies one subpopulation of endothelial cells that proliferates and another that undergoes the endothelial-mesenchymal transition in regenerating pig hearts.Frontiers in bioengineering and biotechnology · 2023Article
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7 authors at 1 institution in 1 country.
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Abstract
Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of individual cells. However, the effectiveness of the currently available tools for processing and interpreting these immense datasets is limited. We incorporated three Artificial Intelligence (AI) techniques into a toolkit for evaluating scRNAseq data: AI Autoencoding separates data from different cell types and subpopulations of cell types (cluster analysis); AI Sparse Modeling identifies genes and signaling mechanisms that are differentially activated between subpopulations (pathway/gene set enrichment analysis), and AI Semisupervised Learning tracks the transformation of cells from one subpopulation into another (trajectory analysis). Autoencoding was often used in data denoising; yet, in our pipeline, Autoencoding was exclusively used for cell embedding and clustering. The performance of our AI scRNAseq toolkit and other highly cited non-AI tools was evaluated with three scRNAseq datasets obtained from the Gene Expression Omnibus database. Autoencoder was the only tool to identify differences between the cardiomyocyte subpopulations found in mice that underwent MI or sham-MI surgery on postnatal day (P) 1. Statistically significant differences between cardiomyocytes from P1-MI mice and mice that underwent MI on P8 were identified for six cell-cycle phases and five signaling pathways when the data were analyzed via Sparse Modeling, compared to just one cell-cycle phase and one pathway when the data were analyzed with non-AI techniques. Only Semisupervised Learning detected trajectories between the predominant cardiomyocyte clusters in hearts collected on P28 from pigs that underwent apical resection (AR) on P1, and on P30 from pigs that underwent AR on P1 and MI on P28. In another dataset, the pig scRNAseq data were collected after the injection of CCND2-overexpression Human-induced Pluripotent Stem Cell-derived cardiomyocytes (
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