基于SVM方法构建细菌sRNA靶标预测模型

Construction of a model for prediction of bacterial sRNA targets using support vector machines

目的:为实验方法鉴定细菌sRNA靶标和研究sRNA功能提供生物信息学支持。方法:首先以实验证实的132个sRNA与靶标相互作用数据为训练集,其中包含46个阳性数据和86个阴性数据;其次,以实验证实的22个阳性数据和随机生成的1 700个阴性数据为测试集;最后以RNA二级结构谱等特征为变量,运用支持向量机(SVM)方法构建sRNA靶标预测数学模型。结果和结论:构建的模型对训练集的敏感性和特异性均为100%,对测试集的敏感性和特异性分别为72.73%和80.65%。所构建的数学模型为实验发现sRNA靶标提供了生物信息学支持。

Objective： To provide bioinformatics support for experimental identification of bacterial sRNA targets and for the study of sRNA functions. Methods： To construct a model for prediction of bacterial sRNA targets, 132 sRNA-mRNA interactions verified by experiments were collected first as the training dataset, which contained 46 positive samples and 86 negative samples. Then, 22 sRNA-mRNA interactions verified by experiments as the positive test dataset and 1700 randomly-generated sRNA-mRNA interactions as the negative test dataset were selected. Finally, support vector machines （SVM） were used to construct the model with the profile of sRNA-mRNA secondary structure as the features. Results and Conclusion ：The model＇s sensitivity and specificity were 100.00% and 100.00% for the training data, and 72.73% and 80.65% for the test dataset, respectively. Therefore, the model provides bioinformatics support for experimental identification of sRNA targets.