DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures.

ABSTRACT: RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal

SUBMITTER: Qiao Y 

PROVIDER: S-EPMC10808905 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature