Predicting microRNA targets in time-series microarray experiments via functional data analysis.

ABSTRACT:

Background

MicroRNA (miRNA) target prediction is an important component in understanding gene regulation. One approach is computational: searching nucleotide sequences for miRNA complementary base pairing. An alternative approach explored in this paper is the use of gene expression profiles from time-series microarray experiments to aid in miRNA target prediction. This requires distinguishing genuine targets from genes that are secondarily down-regulated as part of the same regulatory module. We use a functional data analytic (FDA) approach, FDA being a subfield of statistics that extends standard multivariate techniques to datasets with predictor and/or response variables that are functional.

Results

In a miR-124 transfection experiment spanning 120 hours, for genes with me

SUBMITTER: Parker BJ 

PROVIDER: S-EPMC2648746 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature