ArticleBriefings in bioinformatics2022
SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 21 citations in OpenAlex.
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- AttentionGRN: a functional and directed graph transformer for gene regulatory network reconstruction from scRNA-seq data.Briefings in bioinformatics · 2025Article
- Control of Cellular Differentiation Trajectories for Cancer Reversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.Briefings in bioinformatics · 2024Article
- Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques.Heliyon · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- Single-cell gene regulatory network prediction by explainable AI.Nucleic acids research · 2023Article
- Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.Military Medical Research · 2022Review
- Reconstructing gene regulatory networks of biological function using differential equations of multilayer perceptrons.BMC bioinformatics · 2022Article
- Transcriptional dynamics and epigenetic regulation of E and ID protein encoding genes during human T cell development.Frontiers in immunology · 2022Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using linear models challenging. We present SIGNET, a deep learning-based framework for capturing complex regulatory relationships between genes under the assumption that the expression levels of transcription factors participating in gene regulation are strong predictors of the expression of their target genes. Evaluations based on a variety of real and simulated scRNA-seq datasets showed that SIGNET is more sensitive to ChIP-seq validated regulatory interactions in different types of cells, particularly rare cells. Therefore, this process is more effective for various downstream analyses, such as cell clustering and gene regulatory network inference. We demonstrated that SIGNET is a useful tool for identifying important regulatory modules driving various biological processes.
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