MiRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer.

ABSTRACT: The availability of cross-platform, large-scale genomic data has enabled the investigation of complex biological relationships for many cancers. Identification of reliable cancer-related biomarkers requires the characterization of multiple interactions across complex genetic networks. MicroRNAs are small non-coding RNAs that regulate gene expression; however, the direct relationship between a microRNA and its target gene is difficult to measure. We propose a novel Bayesian model to identify microRNAs and their target genes that are associated with survival time by incorporating the microRNA regulatory network through prior distributions. We assume that biomarkers involved in regulatory networks are likely associated with survival time. We employ non-local prior distributions and a stochast

SUBMITTER: Chekouo T 

PROVIDER: S-EPMC4499566 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature