ArticleBioinformatics (Oxford, England)2025
TRENDY: gene regulatory network inference enhanced by transformer.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- From binding to networks: methods for identifying transcription factor targets in plant systems.Frontiers in plant science · 2026Review
- 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
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
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3 authors.
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Abstract
motivationGene regulatory networks (GRNs) play a crucial role in the control of cellular functions. Numerous methods have been developed to infer GRNs from gene expression data, including mechanism-based approaches, information-based approaches, and more recent deep learning techniques, the last of which often overlook the underlying gene expression mechanisms.
resultsIn this work, we introduce TRENDY, a novel GRN inference method that integrates transformer models to enhance the mechanism-based WENDY approach. Through testing on both simulated and experimental datasets, TRENDY demonstrates superior performance compared to existing methods. Furthermore, we apply this transformer-based approach to three additional inference methods, showcasing its broad potential to enhance GRN inference. AVAILABILITY AND IMPLEMENTATION: Code and data files are available at https://github.com/YueWangMathbio/TRENDY, with DOI: 10.6084/m9.figshare.28236074.
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