ArticleNature communications2020
Machine learning uncovers cell identity regulator by histone code.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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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
28 citing papers in PubMed.
- Recovery From Heart Failure: Microvascular Mechanisms.Circulation · 2026Article
- Transcription factor 4 maintains endothelial cell identity by inhibiting endothelial to mesenchymal transition.Nucleic acids research · 2025Article
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- Cell and tissue reprogramming: Unlocking a new era in medical drug discovery.Pharmacological reviews · 2025Review
- Refate identifies chemical compounds to target trans-regulatory networks for cellular conversion.bioRxiv : the preprint server for biology · 2025Article
- SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.Nucleic acids research · 2025Article
- Early tolerance and late persistence as alternative drug responses in cancer.Nature communications · 2025Article
- Unlocking hematopoietic stem cell potential: integrative computational approaches for genomic and transcriptomic analysis.Frontiers in cell and developmental biology · 2025Review
- Review
- Low RNA stability signifies strong expression regulatability of tumor suppressors.Nucleic acids research · 2023Article
- Extrachromosomal DNA (ecDNA): Unveiling its role in cancer progression and implications for early detection.Heliyon · 2023Review
- Tbx5 maintains atrial identity in post-natal cardiomyocytes by regulating an atrial-specific enhancer network.Nature cardiovascular research · 2023Article
- Low RNA stability signifies increased post-transcriptional regulation of cell identity genes.Nucleic acids research · 2023Article
- Epigenetic landscape reveals MECOM as an endothelial lineage regulator.Nature communications · 2023Article
- Histone Deacetylase 1 Expression and Regulatory Network in Lung Adenocarcinoma Based on Data Mining and Implications for Targeted Treatment.Journal of oncology · 2023Article
- Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis.Nature communications · 2022Article
- Methylation-dependent and -independent roles of EZH2 synergize in CDCA8 activation in prostate cancer.Oncogene · 2022Article
- Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma.Frontiers in genetics · 2022Article
- Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and their expression regulation. Here, we develop CEFCIG, an artificial intelligent framework to uncover CIGs and further define their master regulators. On the basis of machine learning, CEFCIG reveals unique histone codes for transcriptional regulation of reported CIGs, and utilizes these codes to predict CIGs and their master regulators with high accuracy. Applying CEFCIG to 1,005 epigenetic profiles, our analysis uncovers the landscape of regulation network for identity genes in individual cell or tissue types. Together, this work provides insights into cell identity regulation, and delivers a powerful technique to facilitate regenerative medicine.
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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.