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

MicroRNA-target interaction prediction is a difficult task, as most existing tools only provide prediction scores or ranked lists of candidates without offering clear explanations of the supporting evidence or connecting it to a specific biological context. To address this issue, researchers have developed miRAssist, a context-aware evidence integration framework for interpretable miRNA-target prioritization.

miRAssist incorporates six different types of evidence into its analysis: sequence complementarity, thermodynamic stability, sequence conservation, target-site accessibility, functional binding, and functional repression. By generating a sequence-defined candidate universe, the framework initially identified 280,917 potential interactions.

To evaluate the performance of various scoring approaches, the researchers used miRTarBase-supported interactions as positive labels and applied a grouped train/test split by miRNA. Random forest emerged as the most effective method, and it was subsequently selected for further use.

In addition to its robust scoring system, miRAssist also demonstrated superior known-positive enrichment compared to other established miRNA-target prediction models when evaluated against benchmark datasets. To enhance user accessibility, miRAssist includes an LLM-assisted interface that enables natural-language database querying and evidence-grounded summarization of the prioritized candidates.