ArticleNucleic acids research2025
Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- Single-cell mapping of homocysteine-induced perturbations in avian embryogenesis using LMO and BD rhapsody transcriptomics.iScience · 2026Article
- SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.Interdisciplinary sciences, computational life sciences · 2026Article
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- HMA-GCA: hybrid manifold augmentation and gated cross-attention for circRNA-miRNA interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- scRADAR: Dissecting intratumoral drug response heterogeneity at single-cell resolution via mechanism-guided prototype routing.PLoS computational biology · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Transcriptome graph transformer: a graph transformer-based unsupervised model for transcriptome data analysis.BMC bioinformatics · 2026Article
- Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning.BMC biology · 2026Article
- Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.iScience · 2026Article
- Chromatin Accessibility in Cancer: Biological Functions, Mechanisms, Therapeutic Potential, and Future Directions.MedComm · 2026Review
- DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.BMC genomics · 2026Article
- Bioinformatics insights into plant genomic imprinting: approaches, challenges, and future perspectives.Briefings in functional genomics · 2026Review
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Editorial: AI-assisted bioinformatics and functional genomics technologies in medicinal plants.Frontiers in plant science · 2026Article
- Identification and preliminary clinical validation of type 2 diabetes signature genes through machine learning analysis of scRNA-seq data.Frontiers in medicine · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
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Authors and funding
8 authors.
Funding
Abstract
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.
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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.