Combined embedding model for MiRNA-disease association prediction.

ABSTRACT:

Background

Cumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results.

Results

We present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in

SUBMITTER: Liu B 

PROVIDER: S-EPMC7995599 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature