Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data.

ABSTRACT: MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms. Simultaneously, the inverse dataset of negative interactions not likely to occur was extracted to increase classifier performance, as measured using an expansive set of experimentally validated interactions from a variety of sources. In a second differential mode, candidate miRNAs are predi

SUBMITTER: Davis JA 

PROVIDER: S-EPMC5587804 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature