MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features

被引：365

作者：

Jiang, Peng ^{[1
]}

Wu, Haonan ^{[1
]}

Wang, Wenkai ^{[1
]}

Ma, Wei ^{[1
]}

Sun, Xiao ^{[1
]}

Lu, Zuhong ^{[1
]}

机构：

[1] SE Univ, Dept Biol Sci & Med Engn, State Key Lab Bioelect, Nanjing 210096, Peoples R China

来源：

NUCLEIC ACIDS RESEARCH | 2007年 / 35卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1093/nar/gkm368

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one.

引用

页码：W339 / W344

页数：6