sRNASVM——基于SVM方法构建大肠杆菌sRNA预测模型(英文)  

sRNASVM: A MODEL FOR PREDICTION OF SMALL NON-CODING RNAS IN E. coli USING SUPPORT VECTOR MACHINES

作　　者：王立贵[1] 应晓敏[1] 曹源[1] 查磊[1] 李伍举[1] 

机构地区：[1]军事医学科学院基础医学研究所计算生物学中心,北京100850

出　　处：《生物物理学报》2009年第4期287-293,共7页Acta Biophysica Sinica

基　　金：supported by grants from National High Technology Development Program of China (2006AA02Z323);National Sciences Foundation of China (90608004,30470411)~~

摘　　要：在理解细菌与环境的相互作用方面,细菌sRNA的识别发挥重要作用。文章介绍了一个通过增加训练集中实验证实的sRNA来构建细菌sRNA预测模型的策略,并以大肠杆菌K-12的sRNA预测为例来说明策略的可行性。结果表明,按此策略构建的模型sRNASVM的10倍交叉检验精度达到92.45%,高于目前文献中报道的精度。因此,构建的这一模型将为实验发现sRNA提供较好的生物信息学支持。有关模型和详细结果可以从网站http://ccb.bmi.ac.cn/srnasvm/下载。Identification of the bacterial small noncoding RNAs （sRNAs） that plays an important role in understanding interactions between bacteria and their environments. Here the authors introduced a scheme for constructing models for prediction of bacterial sRNAs through incorporating 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/srnasvrm/.

关 键 词：SRNA 支持向量集 预测 

分 类 号：Q937[生物学—微生物学]