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

• URL: http://arxiv.org/abs/2608.12906v1
• Date: Thu, 13 Aug 2026 07:47:44 GMT
• Title: EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction
• Abstract summary: We propose Edge Generation-guided Relation-aware Learning (EGRL)<n>EGRL is a novel framework with several key components.<n> Comprehensive evaluations on four benchmark datasets demonstrate that EGRL achieves competitive overall performance.
• Score: 41.17218504310278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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