DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network

作者：Tingyu Liu, Qiuhao Chen, Renjie Liu, Yuzhi Sun, Yadong Wang, Yan Zhu, Tianyi Zhao · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf179 · 被引用次数：5 · 研究领域：Cancer-related molecular mechanisms research、MicroRNA in disease regulation、Circular RNAs in diseases

Non-coding RNAs (ncRNAs) play crucial roles in drug resistance and sensitivity, making them important biomarkers and therapeutic targets. However, predicting ncRNA-drug associations is challenging due to issues such as dataset imbalance and sparsity, limiting the identification of robust biomarkers. Existing models often fall short in capturing local and global sequence information, limiting the reliability of predictions. This study introduces DMGAT (diffusion map and heterogeneous graph attention network), a novel deep learning model designed to predict ncRNA-drug associations. DMGAT integrates diffusion maps for sequence embedding, graph convolutional networks for feature extraction, and GAT for heterogeneous information fusion. To address dataset imbalance, the model incorporates sensitivity associations and employs a random forest classifier to select reliable negative samples. DMGAT embeds ncRNA sequences and drug SMILES using the word2vec technique, capturing local and global sequence information. The model constructs a heterogeneous network by combining sequence similarity and Gaussian Interaction Profile kernel similarity, providing a comprehensive representation of ncRNA-drug interactions. Evaluated through five-fold cross-validation on a curated dataset from NoncoRNA and ncDR, DMGAT outperforms seven state-of-the-art methods, achieving the highest area under the receiver operating characteristic curve (0.8964), area under the precision-recall curve (0.8984), recall...