mDADGAN: Predicting miRNA-Drug Response Associations Using lncRNAs and a Diffusion-Based Generative Adversarial Network.

MicroRNAs (miRNAs) are key regulators of anticancer drug response, including sensitivity and resistance. Accurate prediction of miRNA-drug response associations is important for understanding response mechanisms and guiding targeted therapy. Existing prediction methods are difficult to fully capture complex high-order biological interactions and are sensitive to data sparsity and class imbalance. In this paper, we present an miRNA-drug response association prediction method called mDADGAN by constructing a diffusion-based generative adversarial network. First, we curated a large-scale and biologically informative data set from four public data resources by aggregating functional associations to facilitate model training. Second, we built a miRNA-lncRNA-drug interaction network by integrating lncRNA into the miRNA-drug network to address data sparsity and employed heterogeneous graph convolution to extract latent features from the interactions among miRNAs, lncRNAs, and drugs. Third, we applied diffusion-based perturbation to denoise and enhance the robustness of feature representations. Finally, we performed cost-sensitive learning to optimize the objective function in an adversarial training process to address class imbalance. The experimental results on four data sets demonstrated that, compared with the existing six methods, mDADGAN achieved higher values for AUC, AUPR, ACC, Recall, and F1-score. The case study demonstrates that integrating lncRNA information effectively enhances miRNA-drug response association prediction. mDADGAN can not only accurately identify known associations in the data sets but also predict previously unreported candidate associations supported by published evidence. Overall, our method mDADGAN has the ability to identify potential miRNA-drug response associations and can provide guidance for future biological experiments to enhance experimental efficiency and reduce experimental costs.