Inferring Disease-Associated MicroRNAs Using Semi-supervised Multi-Label Graph Convolutional Networks.

ABSTRACT: MicroRNAs (miRNAs) play crucial roles in biological processes involved in diseases. The associations between diseases and protein-coding genes (PCGs) have been well investigated, and miRNAs interact with PCGs to trigger them to be functional. We present a computational method, DimiG, to infer miRNA-associated diseases using a semi-supervised Graph Convolutional Network model (GCN). DimiG uses a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations, and tissue expression profiles. DimiG is trained on disease-PCG associations and an interaction network using a GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set from verified disease-miRNA associations. Our results demonstrate th

SUBMITTER: Pan X 

PROVIDER: S-EPMC6817654 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature