miRAssist: a context-aware, evidence integration framework for interpretable miRNA-target prioritization

Motivation: MicroRNA-target interaction prediction remains challenging because many existing tools provide prediction scores or ranked candidate lists without making the supporting evidence easy to interpret or relate to a specific biological context. Results: Here, we developed miRAssist, a context-aware evidence-integration framework for interpretable miRNA-target prioritization. miRAssist integrates six evidence families, including sequence complementarity, thermodynamic stability, sequence conservation, target-site accessibility, functional binding, and functional repression. A sequence-defined candidate universe was generated, resulting in 280,917 candidate interactions. Using miRTarBase-supported interactions as known-positive labels, six supervised scoring approaches were evaluated using a grouped train/test split by miRNA. Random forest showed the strongest performance and was selected. miRAssist also produced stronger known-positive enrichment than established miRNA-target prediction models in the evaluated benchmark. An LLM-assisted interface further supports natural-language database querying and evidence-grounded summarization of prioritized candidates.