ReviewNature reviews. Nephrology2024
Gene regulatory networks in disease and ageing.
Review in Nature reviews. Nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed.
- IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference.International journal of molecular sciences · 2026Article
- Systemic epigenetic dysregulation as a driver of ageing and a therapeutic target.Nature reviews. Molecular cell biology · 2026Review
- A causal reinforcement learning framework for reliable gene regulatory network inference.BMC bioinformatics · 2026Article
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- Enhanced P-TEFb activity compromises dentate gyrus neurogenesis in mice.The EMBO journal · 2026Article
- Gene regulatory and biomolecular networks and their multifaceted biotechnological applications.World journal of microbiology & biotechnology · 2026Review
- CAGAD: dynamic community attention for prediction gene regulatory network.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Epigenetic editing: from concept to clinic.Nature reviews. Drug discovery · 2026Review
- Nascent transcriptome of embryonic genome activation reveals a regulatory axis linking transcriptional priming to early lineage specification in mouse embryos.Nucleic acids research · 2026Article
- Highly Secure In Vivo DNA Data Storage Driven by Genomic Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multilayer metabolomic integration reveals bioenergetic disruption in Long COVID.Journal of translational medicine · 2026Article
- SIGMA: self-supervised inference of gene networks via masked auto-encoding.Frontiers in genetics · 2026Article
- A full life cycle biological clock based on routine clinical data and its impact in health and diseases.Nature medicine · 2025Article
- InCURA: integrative gene clustering based on transcription factor binding sites.Nucleic acids research · 2025Article
- ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.Genomics, proteomics & bioinformatics · 2025Article
- Longitudinal big biological data in the AI era.Molecular systems biology · 2025Review
- TrueProbes: Quantitative Single-Molecule RNA-FISH Probe Design Improves RNA Detection.bioRxiv : the preprint server for biology · 2025Article
- The Life of a Kidney Podocyte.Acta physiologica (Oxford, England) · 2025Review
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- scQTLtools: An R/Bioconductor Package for Comprehensive Identification and Visualization of Single-Cell eQTLs.Biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.
Indexed as
Identifiers
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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.