Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach.

ABSTRACT:

Background

MicroRNAs (miRNAs) are small non-coding RNAs which play a key role in the post-transcriptional regulation of many genes. Elucidating miRNA-regulated gene networks is crucial for the understanding of mechanisms and functions of miRNAs in many biological processes, such as cell proliferation, development, differentiation and cell homeostasis, as well as in many types of human tumors. To this aim, we have recently presented the biclustering method HOCCLUS2, for the discovery of miRNA regulatory networks. Experiments on predicted interactions revealed that the statistical and biological consistency of the obtained networks is negatively affected by the poor reliability of the output of miRNA target prediction algorithms. Recently, some learning approaches have been proposed

SUBMITTER: Pio G 

PROVIDER: S-EPMC4015287 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature