ArticleNature biotechnology2025
Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 90 citations in OpenAlex.
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- CoxFormer enables spatial omics inference with multimodal generative modeling.Nature communications · 2026Article
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- Regulatory and metabolic control of microbial biosynthetic gene clusters.Communications biology · 2026Review
- Reference Regulatory Element-Guided Gene Expression Analysis for Mechanistic Inference of Gene Regulatory Networks.bioRxiv : the preprint server for biology · 2026Article
- NERINE reveals rare variant associations in gene networks across phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease.Cell genomics · 2026Article
- miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.Briefings in bioinformatics · 2026Article
- Robust footprinting with sample-specific Tn5 bias correction for bulk and single cell ATAC-seq.Nature communications · 2026Article
- SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data.Communications biology · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Exploring specialized metabolic pathways in medicinal plants with single-cell and spatial omics.Acta pharmaceutica Sinica. B · 2026Review
- Modeling of gene regulatory networks: an annotated glossary.Trends in plant science · 2026Review
- SC-MO-GRN-DB: A comprehensive repository for single-cell multiomic gene regulatory networks.iScience · 2026Article
- Benchmarking component choices for unpaired single cell RNA and epigenomic integration.Genome biology · 2026Article
- Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026Review
- Gene regulatory and biomolecular networks and their multifaceted biotechnological applications.World journal of microbiology & biotechnology · 2026Review
21 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.
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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.