Higher-Order Dynamic Disentangled Intent Sensing and Bidirectional Joint Updating Framework for NcRNA-Drug Resistance Association Prediction.
Noncoding RNAs (ncRNAs) are critical regulators of drug response and disease progression, making accurate prediction of ncRNA-drug resistance associations a key task in pharmacogenomics and precision medicine. However, current methods largely rely on global neighborhood aggregation, which treats node contexts as homogeneous and overlooks fine-grained structural and semantic heterogeneity. Moreover, they often model ncRNAs and drugs as interchangeable nodes, disregarding their biological distinctions and asymmetric interactions, and failing to effectively integrate modality-specific and cross-modal features. To overcome these limitations, we propose HDBI, a higher-order dynamic disentangled framework for predicting ncRNA-drug resistance associations. HDBI integrates multiview hypergraph learning, disentangled representation modeling, and bidirectional cross-modal updating to capture heterogeneous topological and semantic patterns within ncRNA and drug spaces while preserving modality-specific characteristics and enabling cross-modal information exchange. Extensive experiments on two benchmark data sets demonstrate that HDBI consistently outperforms state-of-the-art methods. Case studies on 5-FU and Docetaxel further support the biological relevance of the predictions, with 22/30 and 21/30 top-ranked ncRNAs supported by PubMed evidence, respectively. Functional enrichment and molecular docking analyses further linked these predictions to drug-relevant pathways and structurally plausible regulatory interactions. These findings suggest that HDBI provides an effective and interpretable framework for prioritizing ncRNA-mediated drug resistance associations and guiding downstream mechanistic investigation.