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Chen, Jake
 
Fu, Yunxin
 
Jiang, Rui
 
Lee, Hoong-Chien
 
Li, Guojun
 
Li, Weizhong
 
Li, Wuju
 
Liu, Tim
 
Ruan, Yijun
 
Tao, Louis
 
Wang, Wen
 
Wang, Xiujie
 
Xu, Ying
 
Zhang, Michael
 
Zhang, Xuegong
 
Li, Wuju

Wuju Li, Ph.D, Professor
E-mail: liwj@nic.bmi.ac.cn, wujuli@yahoo.com
Tel: 86-10-66931324
Fax: 86-10-68213039
Center of Computational Biology
Beijing Institute Of Basic Medical Sciences
Beijing 100850, China
Main interests: Application of RNA secondary structure prediction; Analysis of gene expression profile; ncRNA prediction; Construction of mathematical models for molecular biology experiments.

Tentative Title

Construction of mathematical models for prediction of bacterial sRNA targets

Abstract

Accurate prediction of sRNA targets plays a key role in determining sRNA functions. Here we introduced two mathematical models, sRNATargetNB and sRNATargetSVM, for prediction of sRNA targets using Naive Bayes method and support vector machines (SVM), respectively. The training dataset was composed of 46 positive samples (real sRNA¨C targets interaction) and 86 negative samples (no interaction between sRNA and targets). The leave-one-out cross-validation (LOOCV) classification accuracy was 91.67% for sRNATargetNB, and 100.00% for sRNATargetSVM. To evaluate the performance of the models, an independent test dataset was used, which contained 22 positive samples and 1700 randomly generated negative samples. The results showed that the classification accuracy, sensitivity, and specificity were 93.03%, 40.90%, and 93.71% for sRNATargetNB and 80.55%, 72.73%, and 80.65% for sRNATargetSVM, respectively. Therefore, the presented models provide support for experimental identification of sRNA targets. The related software and supplementary materials can be downloaded from webpage http://www.biosun.org.cn/srnatarget/

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