ReviewBriefings in functional genomics2018
Mapping gene regulatory networks from single-cell omics data.
Review in Briefings in functional genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.
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Who cites it
129 citing papers in PubMed.
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- ScanNet: Single-cell annotation informed by transcriptional regulation Network via iterative heterogeneous graph learning.PLoS computational biology · 2026Article
- CroCoNet: a framework for the quantitative comparison of gene regulatory networks across species.Genome biology · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- SC-MO-GRN-DB: A comprehensive repository for single-cell multiomic gene regulatory networks.iScience · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- STAN, a computational framework for inferring spatially informed transcription factor activity.Nucleic acids research · 2026Article
- scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer.Briefings in bioinformatics · 2025Article
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Immune-mediated cardiac development and regeneration.Seminars in cell & developmental biology · 2025Review
- Harnessing the analog computing power of regulatory networks with the Regulatory Network Machine.iScience · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.Nucleic acids research · 2025Article
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
- Optimized network inference for immune diseased single cells.Frontiers in immunology · 2025Article
- Transformers in single-cell omics: a review and new perspectives.Nature methods · 2024Review
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Stem-cell states converge in multistage cutaneous squamous cell carcinoma development.Science (New York, N.Y.) · 2024Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.
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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.