ReviewNPJ systems biology and applications2023
Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.
Review in NPJ systems biology and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed.
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- The Regulatory Army of Plant Defense: Transcription Factors in the War for Plant Immunity.International journal of molecular sciences · 2026Review
- Reference Regulatory Element-Guided Gene Expression Analysis for Mechanistic Inference of Gene Regulatory Networks.bioRxiv : the preprint server for biology · 2026Article
- Robust footprinting with sample-specific Tn5 bias correction for bulk and single cell ATAC-seq.Nature communications · 2026Article
- T-ChroNet: Time-aware chromatin network reconstruction to detect dynamic regulatory programs in longitudinal epigenetic dataset.NAR genomics and bioinformatics · 2026Article
- RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.Nature communications · 2026Article
- Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.Nucleic acids research · 2026Article
- Chromatin accessibility landscapes define stromal cell identities across tissues.Communications biology · 2026Article
- Challenges and Advances in Bioinformatics and Computational Biology.Current issues in molecular biology · 2026Article
- Evolutionary innovation within conserved gene regulatory networks underlying biomineralized skeletons in Bilateria.Molecular biology and evolution · 2026Article
- Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN.Genome research · 2026Article
- CellCraft: an extensible visual programming application for gene regulatory network inference.Bioinformatics (Oxford, England) · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- GP-DHT: A dual-head transformer with supervised contrastive learning for shared-gene pretraining and single-cell GRN prediction.PloS one · 2026Article
- SIGMA: self-supervised inference of gene networks via masked auto-encoding.Frontiers in genetics · 2026Article
- scGraphVerse: a modular workflow for single-cell gene network inference.Bioinformatics advances · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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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.