A Deep Learning Framework for Accurate lncRNA-Protein Interaction Prediction via Multi-Kernel Graph Embeddings and Self-Attention.

Abstract

Long non-coding RNAs (lncRNAs) play a pivotal role in regulating biological processes, primarily through lncRNA-Protein Interactions (LPI), which are crucial for understanding disease mechanisms and cellular functions. While computational methods exist for predicting LPI, they suffer from suboptimal feature integration, limited generalizability, and inadequate network modeling. To address these challenges, we propose MAKGLPIN (Multi-Architecture, Multi-Kernel Graph-based LPI Network), a novel deep learning framework that integrates sequence features, structural information, and physicochemical properties of lncRNAs and proteins. MAKGLPIN employs a multi-layer Graph Convolutional Network (GCN) enhanced with a self-attention mechanism to capture complex interaction patterns, alongside a Graph Attention Network (GAT) to prioritize critical interactions while preserving long-range dependencies. Extensive evaluations demonstrate that MAKGLPIN outperforms existing methods in robustness, generalizability, and predictive precision across benchmark datasets. Our model provides a computationally efficient and biologically interpretable solution, advancing the study of LPI and their applications in disease research.