ArticleNature communications2023
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 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
71 citing papers in PubMed, 107 citations in OpenAlex.
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- Single-cell transcriptomics reveal PD-1-loss-driven immune dysregulation of pulmonary lymphocytes during earlyMicrobiology spectrum · 2026Article
- Constructing cell-type-specific gene regulatory networks from cell-cell communication and a global gene regulatory network.Briefings in bioinformatics · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Epigenetic programming by H3K23ac defines lineage fate of Meg3Nature cell biology · 2026Article
- R-loops shape the H2A.Z landscape and promote balanced lineage allocation during differentiation.Genome research · 2026Article
- Modeling of gene regulatory networks: an annotated glossary.Trends in plant science · 2026Review
- Characterizing gene perturbations in single cells via network divergence analysis.Nature communications · 2026Article
- Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet.Genome research · 2026Article
- Article
- Cell type-specific gene regulatory network inference from single cell transcriptomics with ctOTVelo.bioRxiv : the preprint server for biology · 2026Article
- Chromatin accessibility landscapes define stromal cell identities across tissues.Communications biology · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
- Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies.Nature communications · 2026Article
- MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference.Frontiers in bioinformatics · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions.ArXiv · 2025Article
11 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
8 authors at 1 institution in 2 countries.
Funding
Abstract
Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.
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.