Hierarchical Semantic Augmentation Graph Neural Network for Drug-Disease Association Prediction

As an essential step in drug intervention discovery, predicting the drug-disease associations (DDAs) explores the potential therapeutic associations in given dugs and diseases. Since the various links in drugs and diseases contain high-order relations and complex therapeutic semantics, Graph Neural Networks (GNNs) have been introduced to DDA predictions and achieved great success. However, most previous approaches require the nodes of given drugs and diseases to have smooth attributes, which is difficult to meet in practical applications. Besides, GNN-based models suffer from the problem of semantic confusion for DDA prediction in heterogeneous graph. These challenges limit the model validity to discover therapeutic semantics in drug-disease networks. To address these challenges in DDA, we propose a novel graph neural network model called HSAGNN to augment node semantics hierarchically with three key steps by applying semantic-guided idea of SGNN method, including topological embedding learning, attribute completion, and semantic-guided aggregation. HSAGNN first learns the topological embedding and adopts the learned topological relationships to complete missing attributes with attention mechanism, which allows the node to contain richer information for neighbor aggregation. Then, the model aggregates the neighbor information with semantic-guided aggregation in both node and semantic levels. Here, HSAGNN injects the learned common knowledge as jumping knowledge to alleviate the semantic confusion. We evaluate the model in DDA tasks with various baselines and explore the model validity with extensive studies. The experimental results show that HSAGNN can discover the potential therapeutic associations by augmented semantics.