GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions.

ABSTRACT: The interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) have been shown to alter gene expression and regulate genes on diseases. Since traditional experimental methods are time-consuming and labor-intensive, most circRNA-miRNA interactions remain largely unknown. Developing computational approaches to large-scale explore the interactions between circRNAs and miRNAs can help bridge this gap. In this paper, we proposed a graph convolutional neural network-based approach named GCNCMI to predict the potential interactions between circRNAs and miRNAs. GCNCMI first mines the potential interactions of adjacent nodes in the graph convolutional neural network and then recursively propagates interaction information on the graph convolutional layers. Finally, it unites the embedded r

SUBMITTER: He J 

PROVIDER: S-EPMC9389118 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature