Hi Dear

I hope the attached paper be usefull

Thanks

I think since all algorithms just model the real world, they all have shortcomings. Even using data from experimentally validated target genes does not mean they are real world target genes. I personally prefer Targetscan. It follows clear real world based rules and tries to detect real target genes, genes that are actually regulated by a microRNA. They just updated their program and have a new publication out that compares most commonly used programs (release 7.0; http://lens.elifesciences.org/05005/index.html; http://www.targetscan.org/). Or as the authors say themselves: "The context++ model was more predictive than any published model and at least as predictive as the most informative in vivo crosslinking approaches."

Hi,

we have just published a paper on ComiRNet (Co-clustered miRNA Regulatory Networks), a web-based database specifically designed to provide biologists and clinicians with user-friendly and effective tools for the study of miRNA-gene target interaction data and for the discovery of miRNA functions and mechanisms. Data in ComiRNet are produced by a combined computational approach based on: 1) a semi-supervised ensemble-based classifier, which learns to combine miRNA-gene target interactions (MTIs) from several prediction algorithms, and 2) the biclustering algorithm HOCCLUS2, which exploits the large set of produced predictions, with the associated probabilities, to identify overlapping and hierarchically organized biclusters that represent miRNA-gene regulatory networks (MGRNs).

It would be interesting to use ComiRNet as a base for the various comparisons.

All the best

Donato

http://www.biomedcentral.com/1471-2105/16/S9/S7