EPMDA: an expression-profile based computational model for microRNA-disease association prediction.

ABSTRACT: MicroRNA has become a new star molecule for understanding multiple biological processes and the mechanism of various complex human diseases. Even though a number of computational models have been proposed for predicting the association between microRNAs and various human diseases, most of them are mainly based on microRNA functional similarity and heterogeneous biological networks which suffer from inevitable computational error and bias. In this work, considering the limitation of information resource used by existing methods, we proposed EPMDA model which is the first computational method using the expression profiles of microRNAs to predict the most potential microRNAs associated with various diseases. Based on the dataset constructed from HMDD v2.0 database, EPMDA obtained AUCs of 0.89

SUBMITTER: Huang YA 

PROVIDER: S-EPMC5675613 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature