Inferring Latent Disease-lncRNA Associations by Faster Matrix Completion on a Heterogeneous Network.

ABSTRACT: Current studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in a variety of fundamental biological processes related to complex human diseases. The prediction of latent disease-lncRNA associations can help to understand the pathogenesis of complex human diseases at the level of lncRNA, which also contributes to the detection of disease biomarkers, and the diagnosis, treatment, prognosis and prevention of disease. Nevertheless, it is still a challenging and urgent task to accurately identify latent disease-lncRNA association. Discovering latent links on the basis of biological experiments is time-consuming and wasteful, necessitating the development of computational prediction models. In this study, a computational prediction model has been remodeled as a matrix compl

SUBMITTER: Li W 

PROVIDER: S-EPMC6749816 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature