Reinforcement Learning-enhanced Dual-view GAT-based Multi-task Learning for Non-coding RNA-Disease Association Prediction

Non-coding RNAs (ncRNAs), particularly long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are important regulators of gene expression and are closely involved in disease pathogenesis. Therefore, identifying ncRNA-disease associations is essential for clarifying disease mechanisms. Because lncRNAs and miRNAs frequently regulate each other in cells, predicting miRNA-disease associations (MDA) and lncRNA-disease associations (LDA) is inherently interconnected. However, many existing computational approaches still treat these tasks as independent problems, which limits their ability to capture cross-task biological signals. In addition, most models use fixed hyperparameters, which may be suboptimal as training dynamics and data characteristics change. To address these issues, we propose RL-DMGLMD (Reinforcement Learning-enhanced Dual-view Multi-task Graph learning for LncRNA-MiRNA-Disease association prediction). RL-DMGLMD contains three key components: (1) a Soft Actor-Critic (SAC) controller that adaptively tunes hyperparameters during training by monitoring loss, validation performance, and gradient information; (2) a unified multi-task framework that jointly predicts LDA, MDA, and lncRNA-miRNA interactions (LMI) using shared encoders with task-specific decoders to enable knowledge transfer; and (3) a dual-view multi-head Graph Attention Network (GAT) that learns from both heterogeneous interaction graphs and attribute graphs to capture relation-specific importance. RL-DMGLMD achieves AUROC values of 0.9900 / 0.9872 / 0.9867 on Dataset 1 and 0.9946 / 0.9903 / 0.9954 on Dataset 2 for LDA, MDA, and LMI, respectively. These results outperform state-of-the-art baselines and support RL-DMGLMD as a practical tool for biomarker discovery and therapeutic target prioritization.