HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.

ABSTRACT: Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interac

SUBMITTER: Chen X 

PROVIDER: S-EPMC5323153 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature