Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model.

ABSTRACT:

Background

MicroRNA regulation is fundamentally responsible for fine-tuning the whole gene network in human and has been implicated in most physiological and pathological conditions. Studying regulatory impact of microRNA on various cellular and disease processes has resulted in numerous computational tools that investigate microRNA-mRNA interactions through the prediction of static binding site highly dependent on sequence pairing. However, what hindered the practical use of such target prediction is the interplay between competing and cooperative microRNA binding that complicates the whole regulatory process exceptionally.

Results

We developed a new method for improved microRNA target prediction based on Dirichlet Process Gaussian Mixture Model (DPGMM) using a large collec

SUBMITTER: Hakguder Z 

PROVIDER: S-EPMC6157162 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature