ArticleBioinformatics (Oxford, England)2023
STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 51 citations in OpenAlex.
- Geneformer-guided multiomics integration identifies Pbx1 as a network hub of hematopoietic stem cell aging.Science advances · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- GRNFormer: accurate gene regulatory network inference using graph transformer.Bioinformatics (Oxford, England) · 2026Article
- Deep Learning-Based Identification of Pathogenicity Genes inPlants (Basel, Switzerland) · 2026Article
- SIGMA: self-supervised inference of gene networks via masked auto-encoding.Frontiers in genetics · 2026Article
- TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data.Computational and structural biotechnology journal · 2026Article
- Decoding yeast transcriptional regulation via a data-and mechanism-driven distributed large-scale network model.Synthetic and systems biotechnology · 2025Article
- GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.Briefings in bioinformatics · 2025Article
- KEGNI: knowledge graph enhanced framework for gene regulatory network inference.Genome biology · 2025Article
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.Briefings in bioinformatics · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.BMC bioinformatics · 2025Article
- TransGeneSelector: using a transformer approach to mine key genes from small transcriptomic datasets in plant responses to various environments.BMC genomics · 2025Article
- GCLink: a graph contrastive link prediction framework for gene regulatory network inference.Bioinformatics (Oxford, England) · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- AttentionGRN: a functional and directed graph transformer for gene regulatory network reconstruction from scRNA-seq data.Briefings in bioinformatics · 2025Article
- HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer.BMC bioinformatics · 2025Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationSingle-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to infer cell-specific gene regulatory networks (GRNs), which is an important challenge in systems biology. Although numerous methods have been developed for inferring GRNs from scRNA-seq data, it is still a challenge to deal with cellular heterogeneity.
resultsTo address this challenge, we developed an interpretable transformer-based method namely STGRNS for inferring GRNs from scRNA-seq data. In this algorithm, gene expression motif technique was proposed to convert gene pairs into contiguous sub-vectors, which can be used as input for the transformer encoder. By avoiding missing phase-specific regulations in a network, gene expression motif can improve the accuracy of GRN inference for different types of scRNA-seq data. To assess the performance of STGRNS, we implemented the comparative experiments with some popular methods on extensive benchmark datasets including 21 static and 27 time-series scRNA-seq dataset. All the results show that STGRNS is superior to other comparative methods. In addition, STGRNS was also proved to be more interpretable than "black box" deep learning methods, which are well-known for the difficulty to explain the predictions clearly. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/zhanglab-wbgcas/STGRNS.
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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.