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

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 th...

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Bibliographic Details
Published in:FEBS letters 2007-04, Vol.581 (8), p.1587-1593
Main Authors: Yan, Xingqi, Chao, Tengfei, Tu, Kang, Zhang, Yu, Xie, Lu, Gong, Yanhua, Yuan, Jiangang, Qiang, Boqin, Peng, Xiaozhong
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Base Sequence
feature selection
Gene Expression Regulation
Humans
Machine learning
MicroRNAs - chemistry
MicroRNAs - genetics
Nucleic Acid Conformation
Prediction
support vector machine
SVM
Target gene
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Summary: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.
ISSN:0014-5793
1873-3468
DOI:10.1016/j.febslet.2007.03.022