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