Hyper Recent

Nucleic acid-binding proteins (NABPs) exhibit cell type-specific regulatory functions, but their target genes and biological roles remain incompletely characterized due to the limitations of current experimental approaches. Here, we present a deep learning framework that integrates gene co-expression correlations to predict NABP regulatory targets and infer their functions across diverse cellular contexts, without requiring binding site or motif information. Substituting low-informative input features with co-expression-derived interactions improved gene expression prediction accuracy. Predicted targets showed strong concordance with ChIP-seq and eCLIP binding sites, and this agreement was significantly greater than for randomly selected gene sets. Functional enrichment and ChatGPT-assisted inference revealed biologically meaningful annotations, including cell type-specific functions such as circadian regulation by AKAP8 in cancer cells and glycolytic control by PKM. Collectively, this integrative framework-combining deep learning, co-expression networks, and large language models-enables the systematic discovery of both known and previously uncharacterized NABP functions in a cell type-specific manner.

Cell type-specific functions of nucleic acid-binding proteins revealed by deep learning on co-expression networks

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