Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 8633-8650· 0 citations· 38 references
MedicineComputer Science
TL;DR
High-order dynamic disentangled framework for predicting ncRNA-drug resistance associations is proposed, suggesting that HDBI provides an effective and interpretable framework for prioritizing ncRNA-mediated drug resistance associations and guiding downstream mechanistic investigation.
Abstract
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.
DC-MetaMG, a deep learning framework based on a causal disentanglement strategy that models association responses as the synergistic interplay between two complementary mechanisms: static binding and dynamic regulation, is proposed, demonstrating its suitability for training scenarios involving complex biological information.
Runzhou Tang, Xi Zhou, Yujie Qi et al.· IEEE journal of biomedical a...· 0 citations
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.
Bowei Zhao, Hui Zhao, Yu-an Huang et al.· IEEE transactions on computa...· 0 citations
Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Xiaoli Lin, Si-Yuan Zhang, Bo Li et al.· Journal of Chemical Informat...· 0 citations
The key innovation of LOGIC lies in constructing a dictionary of functional groups and symptoms, and performing a simple and intuitive multi-hot encoding of drugs and diseases at the micro-scale, and in employing large language models (LLMs) to derive the meso-scale features of diseases without requiring additional domain knowledge.
Yunfei He, Shikai Chen, Yuchen Zhao et al.· IEEE transactions on computa...· 0 citations
Case studies further validated predicted circRNA-drug associations, including cisplatin, enzalutamide, and sorafenib, against published experimental findings, demonstrating HMCDSP's capacity to reveal clinically relevant biomarkers and inform personalized therapeutic strategies.
Yongtian Wang, Wen-Kai Shen, Jiahao Li et al.· Interdisciplinary Sciences C...· 0 citations
Identifying potential microbe–disease associations (MDAs) is vital for elucidating disease pathogenesis and advancing precision medicine. Existing methods primarily learn features from heterogeneous microbe–disease graphs, but often rely solely on global topology for feature propagation, ignoring neighborhood subgraph density, centrality, and edge-weight heterogeneity. The loss of such structural information further exacerbates distributional shifts of microbe–disease feature representations, making it difficult for static concatenation or average fusion to effectively bridge the semantic gaps between modalities or accurately capture the contextual dependencies between node pairs. To address these challenges, we propose STDCAMDA, a dual-channel learning framework for MDA prediction. For structural enhancement, we design a subgraph topology module that fuses global and local topological information via multidimensional edge weights and node-gating mechanisms, thereby encoding rare structural signals while suppressing noise. In feature learning, we adopt a dual-channel strategy: embedding dynamic weight correction into a graph convolutional network for adaptive adjacency calibration and building a cross-pooling attention network to mitigate modality distribution shifts and capture cross-modal dependencies. Finally, we introduce two strategies: a dynamically weighted fusion classifier that integrates dual-channel features and uses a multi-layer perceptron for prediction, and a subgraph-aware negative sampling strategy that selects hard negative samples. Experiments on the Disbiome and HMDAD datasets demonstrate that STDCAMDA outperforms seven existing MDA prediction models, with statistical significance tests confirming the reliability of these improvements and cold-start evaluations validating its robustness and generalization capability. Practically, STDCAMDA provides an effective computational framework for prioritizing candidate disease-related microbes and supporting downstream biomedical validation.
Jiahao Li, Xiangmin Ji, Xiaowen Gao et al.· Journal of King Saud Univers...· 0 citations
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