Prediction of microRNA-disease associations based on distance correlation set.

ABSTRACT:

Background

Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited.

Results

In this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integratin

SUBMITTER: Zhao H 

PROVIDER: S-EPMC5905221 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature