A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

ABSTRACT: The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particularly single-cell transcriptomic data containing rich cell-to-cell variations, it is highly desirable to infer TF-gene interactions (TGIs) using deep learning technologies. Numerous models or software including deep learning-based 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,

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11647272 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature