Measuring Disease Similarity Based on Multiple Heterogeneous Disease Information Networks Quantifying the similarities between diseases is now playing an important role in biology and medicine, which provides reliable reference information in finding similar diseases. Most of the previous methods for similarity calculation between diseases either use a singlesource data or do not fully utilize multisources data. In this study, we propose an approach to measure disease similarity by utilizing multiple heterogeneous disease information networks. Firstly, multiple diseaserelated data sources are formulated as heterogeneous disease information networks which include various types of objects such as disease, pathway, and chemicals. Then, the corresponding subgraphs of these heterogeneous disease information networks are obtained by filtering vertices. Topological scores and semantics scores are calculated in these heterogenous subgraphs using Dynamic Time Warping (DTW) algorithm and meta path method respectively. In this way, we transform multiple heterogeneous disease networks to a homogeneous disease network with different weights on the edges. Finally, the disease nodes can be embedded according to the weights and the similarity between diseases can then be calculated using these  n dimensional vectors. Experiments based on benchmark set fully demonstrate the effectiveness of our method in measuring the similarity of diseases through multisources data.

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.