%0 Journal Article
%T sRNASVM: a model for prediction of small non-coding RNAs in E.coli using support vector machines<br>sRNASVM——基于SVM方法构建大肠杆菌sRNA预测模型
%A 王立贵
%A 应晓敏
%A 曹源
%A 查磊
%A 李伍举
%J 生物物理学报
%D 2009
%I 
%X Identification of bacterial small noncoding RNAs (sRNAs) plays an important role in understanding interactions between bacteria and their environments. Here we introduced a scheme for constructing models for prediction of bacterial sRNAs through incorporating the validated sRNAs into training dataset, and Escherichia coli (E.coli) K-12 was taken as an example to demonstrate the performance of the scheme. The results indicated that the 10-fold cross-validation classification accuracy of the constructed model, sRNASVM, was as high as 92.45%, which had better performance than two existing models. Therefore, the present work provides better support for experimental identification of bacterial sRNAs. The models and detailed results can be downloaded from the webpage http://ccb.bmi.ac.cn/srnasvm/.
%K sRNA
%K Support vector machines
%K Prediction<br>sRNA
%K 支持向量机
%K 预测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=523DF5D55C9EA5CC7E592CA627D240F4&yid=DE12191FBD62783C&vid=C5154311167311FE&iid=E158A972A605785F&sid=EBD6B792C9111B87&eid=2AC7DCCBBC26ECF8&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=0&reference_num=0