An improved random forest-based computational model for predicting novel miRNA-disease associations.

ABSTRACT:

Background

A large body of evidence shows that miRNA regulates the expression of its target genes at post-transcriptional level and the dysregulation of miRNA is related to many complex human diseases. Accurately discovering disease-related miRNAs is conductive to the exploring of the pathogenesis and treatment of diseases. However, because of the limitation of time-consuming and expensive experimental methods, predicting miRNA-disease associations by computational models has become a more economical and effective mean.

Results

Inspired by the work of predecessors, we proposed an improved computational model based on random forest (RF) for identifying miRNA-disease associations (IRFMDA). First, the integrated similarity of diseases and the integrated similarity of miRNAs wer

SUBMITTER: Yao D 

PROVIDER: S-EPMC6889672 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature