HGGN: Prediction of microRNA-Mediated drug sensitivity based on interpretable heterogeneous graph global-attention network

Drug sensitivity significantly influences therapeutic outcomes. Recent discoveries have highlighted the pivotal role of miRNAs in regulating drug sensitivity by modulating genes associated with drug metabolism and action. As crucial regulators of gene expression, miRNAs have emerged as influential factors in determining an individual's response to pharmaceutical interventions. However, current methods for predicting miRNA-drug sensitivity associations overlook the challenges posed by heterogeneous networks and data sparsity. In this paper, we design a dual-channel feature representation strategy for heterogeneous networks and construct HGGN, an interpretable deep learning framework designed to predict miRNA-drug sensitivity associations. HGGN advances beyond traditional approaches by employing a unique dual-channel feature extraction method for miRNAs and drugs, enhancing information retrieval from miRNA-drug networks. It incorporates a Global Attention mechanism to overcome feature propagation interruption in sparse networks. Our experiments on public datasets demonstrate HGGN's superior prediction accuracy over existing methods. The AUC and AUPR metrics of HGGN reached 0.9649 and 0.9610 respectively, and the Accuracy, Precision, Recall and F1 metrics were all above 0.9. We constructed the model toward different negative sample selection strategies with an accuracy gap of less than 1%, which proves the robustness of HGGN. Additionally, HGGN's application in modeling analyses and case studies reveals hidden miRNA-mediated drug sensitivity pathways, showcasing its potential for exploratory analysis.