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.

Predicting genedisease associations via graph embedding and graph convolutional networks Identifying diseaserelated genes provides essential information for physiology, pathology and pharmacology. With the rapid growth of highthroughput sequencing technology and genomewide association studies, a large number of genedisease associations have been accumulated. These associations, coupled with the existing generelated and diseaserelated databases, enable prediction of unknown associations by computational approaches. In this article, we proposed a new graphbased machine learning framework to predict the disease related genes. We first constructed a heterogeneous genedisease association graph by integrating multiple biomedical knowledge bases, and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the genedisease associations. In our method, we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability. We conducted experiments on DisGeNet dataset in 10fold cross validation. The experimental results suggested that our method significantly outperformed the existing methods according to the performance of gene prioritization. The performance comparison of the variations of our method showed the efficacy of each module in our method.