GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.

ABSTRACT: Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potent

SUBMITTER: Wang L 

PROVIDER: S-EPMC7266350 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature