Two-stage clustering based effective sample selection for classification of premiRNAs

被引：0

作者：

Xuan, Ping ^{[1
]}

Guo, Mao-zu ^{[1
]}

Shi, Lei-lei ^{[2
]}

Wang, Jun ^{[1
]}

Liu, Xiao-yan ^{[1
]}

Li, Wen-bin ^{[3
]}

Han, Ying-peng ^{[3
]}

机构：

[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China

[2] Univ Kent, Comp Lab, Canterbury CT2 7NF, Kent, England

[3] Northeast Agr Univ, Soybean Res Inst, Harbin 150030, Heilongjiang, Peoples R China

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE | 2010年

关键词：

PREDICTION; MICRORNAS; FEATURES; REAL;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

To solve the class imbalance problem in classification of pre-miRNAs with ab initio method, a novel sample selection method is proposed according to the characteristics of pre-miRNAs. Real/pseudo premiRNAs are clustered based on their stem similarity and their distribution in high dimensional sample space respectively. The training samples are selected according to the sample density of each cluster. Experimental results are validated by the cross validation and other testing datasets composed of human real/pseudo pre-miRNAs. When compared with the previous study, microPred, our classifier miRNAPred is nearly 12% greater in total accuracy. Our sample selection algorithm is useful to construct more efficient classifier for classification of real premiRNAs and pseudo hairpin sequences.

引用

页码：549 / 552

页数：4

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