Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.

ABSTRACT:

Background

In silico investigations on the integration of multiple datasets are in need of higher statistical power methods to unveil secondary findings that were hidden from the initial analyses. We present here a novel method for the network analysis of messenger RNA post-translational regulation by microRNA molecules. The method integrates expression data and sequence binding predictions through a set of sound machine learning techniques, forwarding all results to an ensemble graph of regulations.

Results

Bayesian network classifiers are induced based on a pool of ensemble graphs with ascending order of complexity. Individual goodness-of-fit and classification performances are evaluated for each learned model. As a testbed, four Alzheimer's disease datasets are integrated

SUBMITTER: Armananzas R 

PROVIDER: S-EPMC6157163 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature