Predicting MicroRNA targets

Abstract

MicroRNAs are a large family of short non-encoding RNAs that regulated protein production by binding to mRNAs. A single miRNA can regulate an mRNA by itself, or several miRNAs can cooperate in regulating the mRNAs. This is all dependent on the degree of complementarity between the miRNA and the target mRNA. Here, we present the program TargetBoost that, using a classifier generated by a combination of hardware accelerated genetic programming and boosting, allows for screening several large dataset against several miRNAs, and computes a likelihood of that genes in the dataset is regulated by the set of miRNAs used in the screening. We also present results from comparison of several different scoring functions for measuring cooperative effects. We found that the classifier used in TargetBoost is best for finding target sites that regulate mRNAs by themselves. A demo of TargetBoost can be found on http://www.interagon.com/demo.