A Multitask Prediction Framework for CircRNAs, Drugs, and Diseases Based on Multi-View Information Integration and Graph Contrastive Learning.

MTP-DDA: Enhanced Drug-Disease Associations Prediction via Multi-task Learning with Multi-view Graph Convolutional Networks and Contrastive Learning

A Novel Graph Transformer Framework for Predicting Drug-Disease Associations with Structural Awareness.

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.

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

Predicting associations between human microbes and drugs (MDA) is a critical step in drug development and precision medicine. Although various computational approaches have been proposed, many existing models still struggle to reveal the key features and interaction mechanisms that drive their predictions, and their decision processes remain difficult to interpret and validate biologically. To address these limitations, we propose IDEAL (Interpretability-Driven Evolvable Attentive Learning for Microbe-Drug Association), a multi-view framework that integrates drug network topological attributes, BERT-encoded drug semantics, drug fingerprints, microbe genome sequence attributes, BERT-encoded microbe semantics, and microbe metabolic pathway attributes. We further introduce GNNExplainer into the graph convolutional network (GCN) pipeline to identify influential drug-microbe relations and feed the resulting structural signal back into graph refinement. In addition, we employ Elastic Weight Consolidation (EWC) to support parameter-efficient adaptation when biological data grow over time while mitigating catastrophic forgetting. Extensive experiments on three public datasets demonstrate that the proposed method achieves strong predictive performance, offers interpretable structural explanations, and provides a practical transfer-learning route for evolving MDA benchmarks. The source code for our model is publicly available at https://github.com/Luoxidu02/IDEAL.

MRGBMDAT: a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction

Abstract Motivation MicroRNAs (miRNAs) are key post-transcriptional regulators involved in diverse biological processes, and their dysregulation is closely associated with the onset and progression of many diseases. Accurate prediction of miRNA-disease association types is therefore essential for understanding disease mechanisms and advancing precision medicine. Although computational methods provide efficient alternatives to wet-lab experiments, existing approaches often focus on binary association prediction, inadequately integrate local semantic dependencies and global topological structures, and suffer from class imbalance. Results To address these limitations, we propose MRGBMDAT, a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction. Specifically, a multi-relational graph convolution module with bidirectional cross-attention captures global topological structures, while a local subgraph sampling module extracts local semantic dependencies. A bilinear fusion decoder with element-wise attention jointly models their linear and nonlinear interactions. In addition, an iterative feature similarity-based negative sample selection strategy is introduced to alleviate class imbalance. Experimental results on the HMDD v3.2 dataset demonstrate that MRGBMDAT significantly outperforms five state-of-the-art methods across multiple evaluation metrics, exhibiting strong discriminative power and generalization capability. Availability and implementation The source code is publicly available at https://github.com/CDMBlab/MRGBMDAT.

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.

MFCAMNet: predicting miRNA-disease associations by multi-feature cascade attention mechanism network

Drive by the rapid envolution of deep-learning techniques, a large body of biological experiments bas has uncovered extensive associations between microRNAs (miRNAs) and complex human diseases, hig- hlighting the pivotal roles of miRNAs in pathogenesis. Elucidating these associations is essential for understanding disease mechanisms and developing preventive strategies. Traditional wet-lab validati- on, however, is notoriously labor- and resource-intensive, creating an urgent demand for efficient computational tools that can prioritize the most promising miRNA–disease candidates. Existing predictors predominantly rely on a single category of handcrafted features, thereby overlooking the complementary information embedded in multiple, heterogeneous data sources. Although a few recent attempts integrate diverse features, they usually exploit only a limited subset and fail to capture the intricate, non-linear relationships among them. To address these limitations, we propose MFCAMNet, a Multi-Feature fusion and Cross-Self-Attention model for MiRNA–Disease association prediction. Firstly, we construct multiple similarity matrices and employ two independent autoencoders with multi-source feature attention to obtain deep features of miRNA and disease to extract the inherent relationships between multiple features. Secondly, the proposed model employs element-level addition, element-level multiplication, and concatenation operations to generate miRNA-disease pair features with rich information. Finally, we use the encoder structure of the transformer to fuse the three deep features and predict all potential miRNA disease associations. We conducted comprehensive evaluations on the public HMDD v2.0 and HMDD v3.2 benchmark datasets. MFCAMNet achieved average AUCs of 0.9455 and 0.9420 under 5-fold and 10-fold cross-validation on HMDD v2.0, respectively, and an AUC of 0.9578 under 5-fold cross-validation on HMDD v3.2, outperforming state-of-the-art competitors. Case studies on breast, esophageal, and lung cancers further corroborate the reliability and practical utility of the proposed method.