Construction of two mathematical models for prediction of bacterial sRNA targets.
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 Nai ve Bayes method and support vector machines (SVM), respectively. The training dataset was composed of 46 positive samples (real sRNA-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/.
Zhao
Yalin
Y
Center of Computational Biology, Beijing Institute of Basic Medical Sciences, Taiping Road 27#, Haidian District, Beijing 100850, China.
Li
Hua
H
Hou
Yanyan
Y
Cha
Lei
L
Cao
Yuan
Y
Wang
Ligui
L
Ying
Xiaomin
X
Li
Wuju
W
D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics; D015964:Gene Expression Regulation, Bacterial; D035683:MicroRNAs / Q000378:metabolism*; D008954:Models, Biological; D012329:RNA, Bacterial / Q000378:metabolism*
D035683:MicroRNAs; D012329:RNA, Bacterial