ISLMI:Predicting lncRNA-miRNA Interactions Based on Information Injection and Second-Order Graph Convolution Network

Studies have shown that IncRNA-miRNA interactions can affect cellular expression at the level of gene molecules through a variety of regulatory mechanisms and have important effects on the biological activities of living organisms. Several biomolecular network-based approaches have been proposed to accelerate the identification of lncRNA-miRNA interactions. However, most of the methods cannot fully utilize the structural and topological information of the lncRNA-miRNA interaction network. In this article, we proposed a new method, ISLMI, a prediction model based on information injection and second order graph convolution network(SOGCN). The model calculated the sequence similarity and Gaussian interaction profile kernel similarity between lncRNA and miRNA, fused them to enhance the intrinsic interaction between the nodes, using SOGCN to learn second-order representations of similarity matrix information. At the same time, multiple feature representations obtain using different graph embedding methods were also injected into the second-order graph representation. Finally, matrix complementation was used to increase the model accuracy. The model combined the advantages of different methods and achieved reliable performance in 5-fold cross-validation, significantly improved the performance of predicting lncRNA-miRNA interactions. In addition, our model successfully confirmed the superiority of ISLMI by comparing it with several other model algorithm.

Automated summary

Studies have shown that IncRNA-miRNA interactions have important effects on gene expression and biological activities. In this research, a new prediction model called ISLMI was proposed, which used information injection and a second order graph convolution network (SOGCN) to enhance the performance of predicting lncRNA-miRNA interactions. The model achieved reliable performance in 5-fold cross-validation and significantly improved the prediction accuracy.