Base-resolution binding profile prediction of proteins on RNAs with deep learning.

ABSTRACT: RNA-binding proteins play crucial roles in various RNA-associated biological processes, which are closely linked to cellular function and disease. Based on CLIP-seq data, the existing deep learning methods are developed to predict protein-RNA interactions. However, CLIP-seq relies on gene expression, which varies significantly across cells. Existing methods are typically trained on peak-associated binding sites and implicitly defined non-binding sites, without considering the cell-specific expression profiles. Given the dynamic nature of protein-RNA interactions, these methods struggle to accurately predict the binding nucleotides and strength of proteins on RNAs across cell lines. Therefore, this study proposes a novel deep learning-based method, iDeepB, designed to predict the proteins b

SUBMITTER: Liu X 

PROVIDER: S-EPMC12341914 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature