Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks.

ABSTRACT:

Background

Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are developed in various small datasets, which each represent a limited sample space. Thus, the prediction accuracy of these tools has not been systematically validated at a larger scale. Accordingly, comparing the prediction accuracy of these tools and determining their applicability become challenging. In addition, the accuracy of these tools, especially in large datasets, needs to be improved for broader applications.

Results

In this project, a large dataset containing more than 46,600 miRNA:mRNA interactions was assembled and split

SUBMITTER: Zhao B 

PROVIDER: S-EPMC6391818 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature