Multi-view Graph Learning Framework with Spectral Encoding and Sparse Cross-Attention for miRNA-Drug Association Prediction.

Authors

Ru Nie, Ying Fu, Zhengwei Li, Lei Wang, Zhanguo Xia, Yujun Zhao

Published in

IEEE transactions on computational biology and bioinformatics. Volume PP. Sep 01, 2026. Epub Sep 01, 2026.

Abstract

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.

PMID:
42678847
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.

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