Fig 5

Flowchart of the proposed GEEL-PI and GEEL-FI. a by integrating the two similarity networks with the known lncRNA-miRNA interaction network, we construct a lncRNA-miRNA heterogeneous network. Different graph embedding methods are applied to the lncRNA-miRNA heterogeneous network to learn low-dimensional representations of lncRNAs and miRNAs. b for GEEL-PI, base predictors are trained based on the learned representations from different embedding methods. Then, their output predictions are integrated for further improving the performance and generalizability. c for GEEL-FI, by constructing a deep attention neural network, we integrate abundant embedded representation of lncRNA and miRNA to obtain distinctive lncRNA-miRNA pair features