Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations.

ABSTRACT: Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biologica

SUBMITTER: Xuan P 

PROVIDER: S-EPMC6769579 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature