Improving microRNA target prediction by performance-based algorithm combination

MicroRNAs have been at the center stage of the biomedical community for more than a decade now, after a team of scientists working at Harvard University in Cam- bridge discovered their vital role in the regulation of gene expression [1]. Since then, the use of bioinformatic tools has become a major accelerator in our understanding of microRNA function. Many algorithms have been created to predict where mi- croRNAs are encoded, as well as what genes they regulate [2–10]. Unfortunately, due to the popularity of the field, it is not always clear which of the available computa- tional methods is best suited for determining which mRNA transcripts are regulated by which microRNAs.

We propose a straightforward method to combine the tens of currently available prediction algorithms, and assign them a credibility measure based on their previous performance to simplify the task of experimental validation.