Predicting DrugDisease Treatment Associations Based on Topological Similarity and Singular Value Decomposition To assist drug development, many computational methods have been proposed to identify potential drugdisease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods need the similarities of drugs and diseases, and they will not work if the biological or chemical features for computing similarities are missing. Besides, being lack of validated negative samples in the drugdisease associations data, most methods simply select some unlabeled samples as negative ones, which may introduce noises. Herein, we propose a new method (TSSVD) which only uses those known drugprotein, diseaseprotein and drugdisease interactions to predict the potential drugdisease associations. In a constructed drugproteindisease heterogeneous network, we consider the common neighbors of drugs and diseases to obtain the topological similarity. Then the topological similarity matrix of drugs (diseases) will be used to get the low dimensional embedding representations of drugdisease pairs. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the kstep neighbors relationships between drugs and diseases. Compared with some stateoftheart methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods.

GNDD: A Graph Neural NetworkBased Method for DrugDisease Association Prediction Potential drugdisease association prediction is important to facilitate drug discovery. However, most of existing drugdisease association prediction approaches rely on assembling multiple drug (disease)related biological information, which is usually not comprehensively available, and they always fail to explore the latent information in drugdisease network. To tackle these challenges, we propose a graph neural networkbased method for drugdisease association prediction, dubbed GNDD, with capturing the complex information between drugs and diseases dispense with any side information. Specifically, GNDD introduces the idea of collaborative filtering in recommendation system to avoid the dependency on multidata. Furthermore, an embedding propagation strategy is exploited to model the highorder relationships in drugdisease network. We conduct experiments on the Comparative Toxicogenomics Database, demonstrating the effectiveness of our method in drugdisease association prediction.