ReviewMolecular systems biology2025
Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.
Review in Molecular systems biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- Biologically informed neural network models are robust to spurious interactions via self-pruning.Bioinformatics (Oxford, England) · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Differential gene regulatory network analysis reveals transcriptional disruption in opioid.NAR genomics and bioinformatics · 2026Article
- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- SC-MO-GRN-DB: A comprehensive repository for single-cell multiomic gene regulatory networks.iScience · 2026Article
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- TEAD4 and RXRA Regulate the Function of Nucleus Pulposus Cells in Intervertebral Disc Degeneration Via the TNF-α/NF-κB Pathway: An Integrated Analysis of Single-Cell RNA-Seq, Bulk RNA-Seq, and In Vitro Validation.Applied biochemistry and biotechnology · 2026Article
- Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- Biologically informed neural network models are robust to spurious interactions via self-pruning.bioRxiv : the preprint server for biology · 2025Article
- LM-Merger: a workflow for merging logical models with an application to gene regulatory network models.BMC bioinformatics · 2025Article
- Deep exploration of logical models of cell differentiation in human preimplantation embryos.NPJ systems biology and applications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for understanding complex cellular regulation. However, the inherent noise and sparsity of scRNA-seq data present significant challenges to accurate GRN inference. This review explores one promising approach that has been proposed to address these challenges: integrating prior knowledge into the inference process to enhance the reliability of the inferred networks. We categorize common types of prior knowledge, such as experimental data and curated databases, and discuss methods for representing priors, particularly through graph structures. In addition, we classify recent GRN inference algorithms based on their ability to incorporate these priors and assess their performance in different contexts. Finally, we propose a standardized benchmarking framework to evaluate algorithms more fairly, ensuring biologically meaningful comparisons. This review provides guidance for researchers selecting GRN inference methods and offers insights for developers looking to improve current approaches and foster innovation in the field.
Indexed as
Identifiers
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.