发表机构
Macau University of Science and Technology; Macau University of Science and Technology Zhuhai Research Institute; Wuhan University of Technology; Jiangsu University; Harbin Institute of Technology（澳门科技大学; 澳门科技大学珠海研究院; 武汉理工大学; 江苏大学; 哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EGRL是一种用于RNA-蛋白质相互作用预测的新型深度学习框架，通过边生成引导的关系感知学习，在未知分子的冷启动场景中相比现有方法实现了显著的性能提升。
AI 中文摘要
RNA-蛋白质相互作用（RPIs）对调控细胞功能至关重要。传统用于RPI检测的湿实验室实验成本高且耗时，而深度学习（DL）方法为RPI预测（RPIP）提供了高效的计算替代方案。其中，图神经网络（GNN）颇具潜力，因为它们能自然地对RPI网络进行建模。然而，现有的基于GNN的方法通常依赖同构图或预定义的元路径，这限制了它们处理数据稀疏性以及泛化到涉及未知分子的冷启动场景的能力。为解决这些局限，我们提出了Edge Generation-guided Relation-aware Learning（EGRL），这是一个具有多个关键组件的新型框架：隐式元路径学习，用于捕捉关系语义而无需人工设计的路径；多关系感知注意力机制，用于自适应融合交互模式；图生成器，用于预测潜在（“软”）边以支持冷启动节点；以及多特征融合预测器，用于最终交互评分。EGRL与主任务损失和辅助生成器损失联合训练。在四个基准数据集上的综合评估表明，EGRL具有竞争力的整体性能。更重要的是，它在冷启动场景中表现出卓越的泛化能力，在未知分子上达到了受试者工作特征曲线下面积（AUROC）为0.867、精确率-召回率曲线下面积（AUPR）为0.861，与先前的最先进方法相比，AUROC提升了8.6%，AUPR提升了5.0%。代码将很快发布。
英文摘要
RNA-Protein Interactions (RPIs) are critical for regulating cellular functions. While traditional wet-lab experiments for RPI detection are costly and time-consuming, Deep Learning (DL) methods provide an efficient computational alternative for RPI Prediction (RPIP). In particular, Graph Neural Networks (GNNs) are promising, as they naturally model RPI networks. However, existing GNN-based methods often rely on homogeneous graphs or predefined meta-paths, which limit their ability to handle data sparsity and to generalize to cold-start scenarios involving unknown molecules. To address these limitations, we propose Edge Generation-guided Relation-aware Learning (EGRL), a novel framework with several key components: implicit meta-path learning to capture relational semantics without handcrafted paths; a multi-relation-aware attention mechanism for adaptive fusion of interaction patterns; a graph generator that predicts potential ("soft") edges to support cold-start nodes; and a multi-feature fusion predictor for final interaction scoring. EGRL is jointly trained with a primary task loss and an auxiliary generator loss. Comprehensive evaluations on four benchmark datasets demonstrate that EGRL achieves competitive overall performance. More importantly, it exhibits superior generalization in cold-start settings, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.867 and an Area Under the Precision-Recall curve (AUPR) of 0.861 on unknown molecules, corresponding to improvements of 8.6% in AUROC and 5.0% in AUPR over prior state-of-the-art methods. The code will be released soon.
