Improving the prediction of human microRNA target genes by using ensemble algorithm

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Abstract
MicroRNAs are a class of small endogenous noncoding RNAs which play important regulatory roles mainly by post-transcriptional depression. Finding miRNA target genes will help a lot to understand their biological functions. We developed an ensemble machine learning algorithm which helps to improve the prediction of miRNA targets. The performance was evaluated in the training set and in FMRP associated mRNAs. Moreover, using human mir-9 as a test case, our classification was validated in 9 of 15 transcripts tested. Finally, we applied our algorithm on the whole prediction data set provided by miRanda website. The results are available at http://www.biosino.org/~kanghu/mRTP/mRTP.html.