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From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF\u2013gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning\u2013based algorithms have been designed to identify transcriptional regulatory relationships between TFs and the downstream genes. However, these methods do not significantly improve predictions of TGIs due to some limitations regarding constructing underlying interactive structures linking regulatory components. In this study, we introduce a deep learning framework, DeepTGI, that encodes gene expression profiles from single-cell and\/or bulk transcriptomic data and predicts TGIs with high accuracy. Our approach could fuse the features extracted from Auto-encoder with self-attention mechanism and other networks and could transform multihead attention modules to define representative features. By comparing it with other models or methods, DeepTGI exhibits its superiority to identify more potential TGIs and to reconstruct the GRNs and, therefore, could provide broader perspectives for discovery of more biological meaningful TGIs and for understanding transcriptional gene regulatory mechanisms.<\/jats:p>","DOI":"10.1093\/bib\/bbae639","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:30:46Z","timestamp":1734345046000},"source":"Crossref","is-referenced-by-count":3,"title":["A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks"],"prefix":"10.1093","volume":"26","author":[{"given":"Yong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Electronic Information, Guangxi Minzu University , 188 East University Road, Nanning, Guangxi , 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