BioLLMNet: enhancing RNA-interaction prediction with a specialized cross-LLM transformation network.

ABSTRACT: Ribonucleic acids (RNAs) play a central role in cellular processes by interacting with proteins, small molecules, and other RNAs. Accurate prediction of these interactions is critical for understanding post-transcriptional regulation and advancing RNA-targeted therapeutics. However, existing computational methods are limited by their reliance on hand-crafted features, modality-specific architectures, and often require structural or physicochemical data, which are experimentally challenging to obtain and unavailable for many RNA molecules. These constraints hinder generalizability and fail to capture the complex, context-dependent semantics of RNA interactions. We present BioLLMNet, a unified sequence-only framework that leverages pretrained biological language models to encode rich, contex

SUBMITTER: Abir AR 

PROVIDER: S-EPMC12554096 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature