TEMCL: Prediction of Drug-Disease Associations Based on Transformer and Enhanced Multi-View Contrastive Learning

Drug repositioning (DR) has emerged as an effective method of identifying new indications for existing drugs. Many DR methods have demonstrated superior performance. However, most of them utilize a limited number of biological entities, ignoring the critical role of other entities in addressing data sparsity as well as improving model generalization capabilities. In addition, fully capturing high-order information of biological data still needs to be fully explored. To address above issues, a model based on transformer and enhanced multi-view contrastive learning (TEMCL) is proposed for predicting drug-disease associations (DDAs). Firstly, transformer is employed to obtain high-order features of nodes from similarity information. Secondly, based on similarity matrices and association matrices of nodes, two different types of views are constructed, i.e., homogeneous hypergraphs and heterogeneous association graphs. Among them, to alleviate sparsity problem existing in heterogeneous graphs, protein nodes as well as meta-path enhancement strategy are introduced. Thirdly, hypergraph convolutional network and heterogeneous graph transformer are used to extract node features on above two types of views, respectively. Contrastive learning is applied to obtain more representative features. Finally, multilayer perceptron (MLP) is used for predicting DDAs. Experiments show that TEMCL outperforms existing methods on DR task, exhibiting superior performance. In addition, case studies further demonstrate the effectiveness of this model. TEMCL provides new insights for identifying novel DDAs.