HGANTLDA: A Hybrid Framework Integrating Sequence Language Modeling and Heterogeneous Graph Attention for lncRNA-Disease Association Prediction

LncRNAs have been confirmed by various studies to play an important role in the generation of a variety of diseases, making the accurate prediction of IncRNA-disease associations crucial for diagnosis and therapy. However, experimentally validated associations remain scarce. To address the limitations of existing computational methods regarding biological information coverage and model robustness, we developed HGANTLDA, a novel multimodal information fusion framework. We made three key contributions: 1) integrating the Nucleotide Transformer, a gene language pre-training model, to systematically encode IncRNA sequence semantic information, enhancing sequencelevel feature representation; 2) incorporating miRNA regulatory mechanisms by constructing a heterogeneous graph of IncRNA, miRNA, and disease nodes, and employing a HAN to learn representations from complex semantic paths among these nodes; 3) developing a function similarity-based negative sample selection strategy that significantly reduced pseudo-negative sample interference, effectively improving prediction stability. Extensive experimental results demonstrate that HGANTLDA achieves superior performance, with an AUC of 98.93% and an F1-score of 97.29%, representing a 2.7% improvement over the secondranked model. We have also developed an accessible online service system (http://62.234.10.79:80/) that allows users to perform predictions, customize models, and visualize IncRNA-disease association results interactively.