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Improving the prediction of human microRNA target genes by using ensemble algorithm
Author(s) -
Yan Xingqi,
Chao Tengfei,
Tu Kang,
Zhang Yu,
Xie Lu,
Gong Yanhua,
Yuan Jiangang,
Qiang Boqin,
Peng Xiaozhong
Publication year - 2007
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2007.03.022
Subject(s) - microrna , computer science , set (abstract data type) , gene , class (philosophy) , algorithm , machine learning , computational biology , artificial intelligence , test set , ensemble learning , training set , data mining , biology , genetics , programming language
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.