ArticlePLoS genetics2023
Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model.
Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics.Synthetic and systems biotechnology · 2027Article
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- 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
- SpaMFG: a spatial multi-omics integration method based on feature grouping.Bioinformatics (Oxford, England) · 2026Article
- Inferring cell-specific gene regulatory networks based on causal graph embedding.Cell reports methods · 2026Article
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.Bioinformatics (Oxford, England) · 2026Article
- Transferosomes as Advanced Nanocarriers for Enhanced Drug Delivery in Skin Cancer Therapy: A Comprehensive Review.Applied biochemistry and biotechnology · 2026Review
- Towards a hierarchical approach and scalable semi-local centrality for key node identification in weighted complex networks.Scientific reports · 2026Article
- Hybrid fuzzy machine learning models optimized with meta-heuristics for accurate EEG-based neurological assessment.Scientific reports · 2026Article
- CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Simultaneous electrochemical detection of imipenem and meropenem using a Pt-Au bimetallic nanoparticle-decorated 3D graphene oxide modified glassy carbon electrode.Scientific reports · 2026Article
- New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification.Journal of imaging informatics in medicine · 2026Article
- Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction.Journal of imaging informatics in medicine · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- Microbial Omics.Progress in molecular and subcellular biology · 2026Review
- GP-DHT: A dual-head transformer with supervised contrastive learning for shared-gene pretraining and single-cell GRN prediction.PloS one · 2026Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.
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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.