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.

Predicting genedisease associations from the heterogeneous network using graph embedding The discovery of genedisease associations is important for the prevention, diagnosis and treatment of diseases. The studies on genedisease associations have produced diverse data, which can facilitate the genedisease association prediction. Integrating diverse information is critical for developing highaccuracy prediction models. In this paper, we propose a heterogeneous networkbased method that enhances genedisease association prediction by using graph embedding and ensemble learning, abbreviated as xe2x80x9cHNEEMxe2x80x9d. A heterogeneous network is constructed based on genedisease associations, genechemical associations and diseasechemical associations, to combine diverse information. The network uses genes, diseases and chemicals as nodes, and uses their associations as edges. The graph embedding methods are utilized to extract representation vectors of nodes in the heterogeneous network, and the feature vectors of genes and diseases are merged to represent genedisease pairs, and the random forest is employed to build the prediction model based on genedisease pairs. We consider six types of graph embedding methods, and take the individual graph embedding methodgenerated features to build prediction models and use them as base predictors, and then combine base predictors to develop the ensemble learning model HNEEM. We comprehensively compare different graph embedding methods, and results demonstrate that the graph embedding methods produce satisfying results in the genedisease association prediction, and integrating different graph embedding methods can make further improvements. In computational experiments, HNEEM produces better results compared to the stateoftheart genedisease perdition methods, and HNEEM is robust to the data richness as well. Moreover, the usefulness of the proposed method HNEEM is validated by the case studies. In conclusion, HNEEM is a promising method for predicting genedisease associations.