LncRNAmiRNA interaction prediction from the heterogeneous network through graph embedding ensemble learning LncRNAmiRNA interactions play crucial roles in gene regulatory networks and can reveal functions of lncRNAs and miRNAs. Although several methods have been proposed to infer interactions on the lncRNAmiRNA interaction network, few attentions have been paid to fully exploiting the structure of lncRNAmiRNA interaction network. In this paper, we propose a Graph Embedding Ensemble Learning method (abbreviated as xe2x80x9cGEELxe2x80x9d) to predict lncRNAmiRNA interactions. First, we collect lncRNA sequences and miRNA sequences to calculate lncRNAlncRNA sequence similarity and miRNAmiRNA sequence similarity, and then we combine them with the known lncRNAmiRNA interactions to construct a heterogeneous network, which takes lncRNAs and miRNAs as nodes. We adopt graph embedding methods to learn representations of lncRNAs and miRNAs from the heterogeneous network, and then merge the representations of lncRNAs and miRNAs to represent the lncRNAmiRNA pairs. Random forest classifiers are built based on the merged representations to predict lncRNAmiRNA interactions. We consider five different graph embedding methods, and evaluate the corresponding models. Further, we use individual graph embedding methodbased model as base predictors and build a highlevel ensemble model. The experimental results show that GEEL achieves AUPR score of 0.7004 and AUC score of 0.9537, and outperforms base predictors and other stateoftheart methods. In conclusion, GEEL is an effective tool for lncRNAmiRNA interaction prediction.

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.