FusionMVSA: Multi-View Fusion Strategy With Self-Attention for Enhancing Drug Recommendation

Leveraging the wealth of biomedical data available, we can derive insights into the relationships between biological entities from various angles. This underscores the complexity and significance of developing a dynamic approach for integrating data from multiple sources, a critical endeavor in drug recommendation. In this study, we introduce an innovative deep learning approach termed "Multi-View Fusion Strategy with Self-Attention" (FusionMVSA), designed to predict associations between drugs and diseases. To effectively amalgamate data from diverse sources and extract representative features, we have developed a feature extraction mechanism that capitalizes on similarities. This mechanism computes self-attention across multiple perspectives using shared group parameters, thereby highlighting common characteristics. Simultaneously, we utilize biomedical similarities among multi-source data as guiding factors for calculating similarity, enabling the capture of more nuanced features. Subsequently, we integrate these features through a feature fusion process, where known associations between drugs and diseases act as guiding terms. This strategy allows us to uncover the complementary aspects of different viewpoints. Ultimately, we predict potential drug-disease associations using a multi-layer perceptron neural network. Our methodology has undergone rigorous testing through various cross-validation experiments and case studies. We are confident that FusionMVSA will prove to be a valuable tool in drug recommendation, offering new avenues for exploration and discovery in the quest to combat diseases.