Chemoresistance is a major contributor to cancer treatment failure, and microRNAs (miRNAs) play a critical role in mediating this resistance by regulating gene expression. Therefore, identifying miRNA-drug associations is of great significance for advancing cancer therapy. However, existing computational models face significant challenges, including heterogeneous feature integration and data sparsity. To overcome these limitations, we propose a novel Multi-view Graph Learning Framework with Spectral Encoding and Sparse Cross-Attention (MVGSCA) for predicting miRNA-drug associations. The model constructs node features based on miRNA sequence similarity and drug SMILES similarity. Then it builds two distinct graphs: a gene-mediated functional graph from miRNA-drug target interactions and an association-guided structural graph from known miRNA-drug associations. These two graphs are linearly combined to produce a collaborative feature representation. To capture both local and global topological features, the model applies local power filtering and global heat kernel diffusion, followed by spectral encoding via Poisson-Charlier polynomial approximation to enhance the feature representation. Furthermore, a sparse cross-attention mechanism is introduced to dynamically weight and integrate heterogeneous features from multiple sources. On a benchmark dataset with 8,720 associations, MVGSCA achieves an AUC of 96.32% and an AUPR of 95.69% under five-fold cross-validation, significantly outperforming six state-of-the-art methods. Experimental results show that MVGSCA effectively integrates heterogeneous biological information and achieves superior prediction performance, offering valuable insights into cancer resistance mechanisms and supporting drug discovery efforts.
Ru Nie, Ying Fu, Zhengwei Li et al.· IEEE transactions on computa...· 0 citations
Simple Summary Circular ribonucleic acid molecules help regulate gene activity and may contribute to many diseases. However, laboratory experiments can examine only a small fraction of the possible links between these molecules and diseases, making it difficult to identify the most promising candidates for further study. This study developed a computer-based method that combines several types of biological information to predict likely relationships between circular ribonucleic acids and diseases. When tested on a widely used collection of known associations, the method showed a strong ability to distinguish known associations from unconfirmed ones. It also performed well on two related collections involving other types of ribonucleic acid. In a liver cancer case study, 19 of the 20 highest-ranked circular ribonucleic acids were supported by published studies, while one remained a potentially new candidate. These findings suggest that the method can help researchers select promising associations for laboratory testing, reduce unnecessary experimental screening, and support the discovery of disease-related markers and treatment targets.
Mianshuo Lu, Mengmeng Wei, Changchun Liu et al.· Biology· 0 citations
Experiments on three cross-patient scRNA-seq data sets demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods.
Yue C. Li, Mengmeng Wei, Xinfei Wang et al.· Journal of Chemical Informat...· 0 citations
MuSeL is proposed, a multi-scale adaptive graph representation learning framework that jointly mitigates the above issues from two complementary perspectives: global topology modeling and local structure optimization.
Yuehu Wu, Lei Wang, Zhengwei Li et al.· Proceedings of the 32nd ACM...· 0 citations
A novel GNN framework, APKAGN, designed for predicting miRNA-disease associations significantly enhances the accuracy of MDAs prediction, offering a powerful tool for investigating disease mechanisms and identifying biomarkers.
Ru Nie, Yingkai Li, Zhengwei Li et al.· IEEE transactions on computa...· 0 citations
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