Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.

ABSTRACT:

Background

Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed miRNA-disease associations. Thus, computational models predicting unidentified miRNA-disease pairs in an efficient way are becoming promising research topics. Although existing methods have performed well to reveal unidentified miRNA-disease associations, more work is still needed to improve prediction performance.

Results

In this work, we present a novel multiple meta-paths fusion graph embedding model to predict unidentified miRNA-disease associations (M2GMDA). Our method takes full advantage of the complex structure and rich semantic information of miRNA-di

SUBMITTER: Zhang L 

PROVIDER: S-EPMC7579830 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature