T-cell receptor (TCR)-epitope binding prediction is critical for immunotherapies but remains challenged by sparse interaction networks and severe class imbalance in training data. Current graph neural network (GNN) approaches for predicting TCR-epitope binding (TEB) fail to address two key limitations: over-smoothing during message propagation in sparse TCR-epitope graphs and biased predictions toward dominant epitope-TCR pairs. Here, we present GRAPE (Graph-Regularized Attentive Protein Embeddings), a framework unifying spectral graph regularization and imbalance-aware learning. GRAPE first leverages protein language models (ESM-2) to generate evolutionary-informed TCR/epitope embeddings, constructing a topology-aware interaction graph. To mitigate over-smoothing, we introduce spectral graph regularization, explicitly constraining node feature smoothness to preserve discriminative patterns in sparse neighborhoods. Simultaneously, a dynamic edge reweighting module prioritizes unobserved TCR-epitope edges during graph propagation, coupled with a differentiable area under the ROC curve-maximization objective that directly optimizes for imbalance resilience. Extensive benchmarking on public datasets demonstrates that GRAPE significantly outperforms state-of-the-art methods in TEB prediction. This work establishes GRAPE as a robust framework for elucidating TCR-epitope interactions, with broad applications in immunology research and therapeutic design.

Plant RNAs are crucial for plant gene expression and protein synthesis. They modulate the spatial structure of themselves and associated molecules, thereby influencing transcription, translation and gene expression regulation. Molecular biology experiments enhance our understanding of plant RNA-RNA interactions (RRIs), yet their complex structure and dynamic properties render these experiments expensive and time-consuming. Recent advances in deep learning have transformed plant RNA research and improved RRI prediction efficiency. However, these methods still struggle with poor prediction accuracy. To address this, this study proposes an interpretable graph representation model for accurate plant RRI prediction. The model enriches sample information by extracting features of different bases from plant RNA data and reconstructs these features using an algorithmic hierarchy approach to capture more complex patterns. A graph representation based on a masking strategy and regularization enhances RNA feature extraction. Furthermore, an RRI modeling approach combining Kolmogorov-Arnold Networks (KAN) and multi-scale fusion is proposed to deeply resolve the complex dynamic interaction mechanisms of RRIs and improve model interpretability. Performance evaluations and case studies on publicly available datasets demonstrate that the proposed model can accurately identify potential RRIs, indicating its potential as a powerful tool for plant gene function annotation. Our data and code are available at: https://github.com/Lqingquan/IGRL-RRI.

Circular RNAs (circRNAs) play pivotal roles in various biological processes and disease progression, particularly in modulating drug responses and resistance mechanisms. Accurate prediction of circRNA-drug associations (CDAs) is essential for biomarker discovery and the advancement of therapeutic strategies. Although several computational approaches have been proposed for identifying novel circRNA therapeutic targets, their performance is often limited by inadequate modeling of higher-order geometric information within circRNA-drug interaction networks. To overcome these challenges, we propose G2CDA, a geometric graph representation learning framework specifically designed to enhance the identification of CDAs and facilitate therapeutic target discovery. G2CDA introduces torsion-based geometric encoding into the message propagation process of the circRNA-drug network. For each potential association, we construct local simplicial complexes, extract their geometric features, and integrate these features as adaptive weights during message propagation and aggregation. This design promotes a richer understanding of local topological structures, thereby improving the robustness and expressiveness of learned circRNA and drug representations. Extensive benchmark evaluations on public datasets demonstrate that G2CDA outperforms state-of-the-art CDA prediction models, particularly in identifying novel associations. Case studies further confirm its effectiveness by uncovering potential drug interactions with the ALDH3A2 and ANXA2 biomarkers. Collectively, G2CDA provides a robust and interpretable framework for accelerating circRNA-based therapeutic target discovery and streamlining drug development pipelines. Our code are archived in: https://github.com/lizhen5000/G2CDA.