ArticleCurrent opinion in systems biology2017
Inference of cell type specific regulatory networks on mammalian lineages.
Article in Current opinion in systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Recovering time-varying networks from single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- ChIP-DIP maps binding of hundreds of proteins to DNA simultaneously and identifies diverse gene regulatory elements.Nature genetics · 2024Article
- CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics.Bioinformatics (Oxford, England) · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
- Bioengineering the human spinal cord.Frontiers in cell and developmental biology · 2022Review
- Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome.Frontiers in immunology · 2022Article
- Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq.Frontiers in bioinformatics · 2021Article
- Topological structure analysis of chromatin interaction networks.BMC bioinformatics · 2019Article
- Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development.Cell systems · 2019Article
- Subtype-specific regulatory network rewiring in acute myeloid leukemia.Nature genetics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Transcriptional regulatory networks are at the core of establishing cell type specific gene expression programs. In mammalian systems, such regulatory networks are determined by multiple levels of regulation, including by transcription factors, chromatin environment, and three-dimensional organization of the genome. Recent efforts to measure diverse regulatory genomic datasets across multiple cell types and tissues offer unprecedented opportunities to examine the context-specificity and dynamics of regulatory networks at a greater resolution and scale than before. In parallel, numerous computational approaches to analyze these data have emerged that serve as important tools for understanding mammalian cell type specific regulation. In this article, we review recent computational approaches to predict the expression and sequence-based regulators of a gene's expression level and examine long-range gene regulation. We highlight promising approaches, insights gained, and open challenges that need to be overcome to build a comprehensive picture of cell type specific transcriptional regulatory networks.
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What OpenQuestion holds
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.