RFMirTarget: predicting human microRNA target genes with a random forest classifier.

ABSTRACT: MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been d

SUBMITTER: Mendoza MR 

PROVIDER: S-EPMC3724815 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature