ArticleResearch (Washington, D.C.)2023
Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator.
Article in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Prediction of Digestible and Metabolizable Energy in Swine Feed Using Machine Learning.ACS omega · 2026Article
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Machine and Deep Learning Reveal Sequence Determinants Encoding Bivalent Histone Modifications.Communications biology · 2026Article
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Deciphering Sequence Determinants of Zygotic Genome Activation Genes: Insights From Machine Learning and the ZGAExplorer Platform.Cell proliferation · 2025Article
- Beyond metaphor: quantitative reconstruction of Waddington landscape and exploration of cellular behavior.Briefings in bioinformatics · 2025Review
- Multi-omics integrative analysis reveals novel genetic loci and candidate genes for ischemic stroke.Molecular therapy. Nucleic acids · 2025Article
- Reconstructing Waddington Landscape from Cell Migration and Proliferation.Interdisciplinary sciences, computational life sciences · 2025Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- The intrinsic dimension of gene expression during cell differentiation.Nucleic acids research · 2025Article
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- Machine Learning-Based identification of resistance genes associated with sunflower broomrape.Plant methods · 2025Article
- EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species.Briefings in bioinformatics · 2025Article
- Alternative splicing dynamics during gastrulation in mouse embryo.Scientific reports · 2025Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
- Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq.IET systems biology · 2024Article
- Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.IET systems biology · 2024Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
- Rewriting cellular fate: epigenetic interventions in obesity and cellular programming.Molecular medicine (Cambridge, Mass.) · 2024Review
- An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.Fundamental research · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.
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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.