ArticleFrontiers in genetics2019
Machine Learning of Stem Cell Identities From Single-Cell Expression Data via Regulatory Network Archetypes.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- 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
- Deep learning models will shape the future of stem cell research.Stem cell reports · 2023Review
- A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.Briefings in bioinformatics · 2021Review
- Epithelial-Mesenchymal Transition Drives Three-Dimensional Morphogenesis in Mammalian Early Development.Frontiers in cell and developmental biology · 2021Article
- A novel machine learning based approach for iPS progenitor cell identification.PLoS computational biology · 2019Article
- Machine Learning of Stem Cell Identities From Single-Cell Expression Data via Regulatory Network Archetypes.Frontiers in genetics · 2019Article
- Modeling Mammalian Commitment to the Neural Lineage Using Embryos and Embryonic Stem Cells.Frontiers in physiology · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the "average" pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells-corresponding to naïve and formative pluripotent states and an early primitive endoderm state-and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.
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Registered trials
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