DDA-NG: Fusing Non-negative Matrix Factorization and Graph Tranformer to Enhance Potential Dmg-Disease Associations

Predicting drug-disease relationships is crucial in the biomedical field, but traditional laboratory experiments are time-consuming and costly, often lacking predictive accuracy and efficiency. This paper presents the DDA-NG model, which integrates Non-negative Matrix Factorization (NMF) and Graph Transformer (GT) to predict drug-disease associations. The model utilizes NMF to extract features from drugs and diseases, capturing global structural information. It then integrates various similarity data and captures higher-order structural relationships through the Graph Transformer. By fusing features from both modules, DDA-NG enhances feature diversity and prediction accuracy. Experimental results show significant performance improvements across multiple datasets, demonstrating its strong capability to identify potential drugs for diseases.