title: Improving the prediction of human microRNA target genes by using ensemble algorithm.
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.
authors: The National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Tsinghua University, Beijing, China. yanxingqi@126.com
authors: Xingqi
authors: X
authors: Yan
authors: Tengfei
authors: T
authors: Chao
authors: Kang
authors: K
authors: Tu
authors: Yu
authors: Y
authors: Zhang
authors: Lu
authors: L
authors: Xie
authors: Yanhua
authors: Y
authors: Gong
authors: Jiangang
authors: J
authors: Yuan
authors: Boqin
authors: B
authors: Qiang
authors: Xiaozhong
authors: X
authors: Peng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D005786:Gene Expression Regulation; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry* / Q000235:genetics*; D009690:Nucleic Acid Conformation
chemical_list: D035683:MicroRNAs