ArticleScience advances2022
DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Mapping enhancer-gene regulatory interactions from single-cell data.Nature genetics · 2026Article
- SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data.Communications biology · 2026Article
- Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.Nucleic acids research · 2026Article
- PAIR: Reconstructing Single-Cell Open-Chromatin Landscapes for Transcription Factor Regulome Mapping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.Nature communications · 2026Article
- Multi-Tissue Omics Analysis Uncovers Translational Regulation Underlying Complex Traits in Pigs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Endothelin-2 from Keratinocytes and its Association with Itch in Skin Diseases.The Journal of investigative dermatology · 2026Article
- CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.Bioinformatics (Oxford, England) · 2026Article
- MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics.bioRxiv : the preprint server for biology · 2026Article
- LONMF: a non-negative matrix factorization model based on graph Laplacian and optimal transmission for paired single-cell multi-omics data integration.BMC bioinformatics · 2025Article
- scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics.Nature communications · 2025Article
- BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data.bioRxiv : the preprint server for biology · 2025Article
- The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.Nature communications · 2025Article
- Analysis of changes in intercellular communications in Alzheimer's disease reveals conserved changes in glutamatergic transmission in mice and humans.Scientific reports · 2025Article
- Leveraging transcription factor physical proximity for enhancing gene regulation inference.Bioinformatics (Oxford, England) · 2025Article
- A large-scale benchmark for network inference from single-cell perturbation data.Communications biology · 2025Article
- An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.Genes to cells : devoted to molecular & cellular mechanisms · 2025Article
- A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.Nature communications · 2025Article
- Non-coding variation in dementias: mechanisms, insights, and challenges.NPJ dementia · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)-to-gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET's predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation-specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.
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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.