EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction

Researchers propose EGRL, a graph neural network framework for RNA-protein interaction prediction that uses implicit meta-path learning, multi-relation-aware attention, and a graph generator to handle cold-start scenarios. On four benchmark datasets, EGRL achieves an AUROC of 0.867 and AUPR of 0.861 on unknown molecules, improving over prior state-of-the-art by 8.6% and 5.0%, respectively.

arXiv:2608.12906v1 Announce Type: new Abstract: 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.