Disease Gene Prediction Based on Heterogeneous Probabilistic Hypergraph Ranking In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariaterelationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.

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.