sipred predicting sirna efficacy using various characteristic methods使用各种特征方法sipred预测sirna功效.pdfVIP

siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods 1 1 1,2,3,4 Wei-Jie Pan , Chi-Wei Chen , Yen-Wei Chu * 1 Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan, 2 Biotechnology Center, National Chung Hsing University, Taichung, Taiwan, 3 Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan, 4 Graduate Institute of Biotechnology, National Chung Hsing University, Taichung, Taiwan Abstract Small interfering RNA (siRNA) has been used widely to induce gene silencing in cells. To predict the efficacy of an siRNA with respect to inhibition of its target mRNA, we developed a two layer system, siPRED, which is based on various characteristic methods in the first layer and fusion mechanisms in the second layer. Characteristic methods were constructed by support vector regression from three categories of characteristics, namely sequence, features, and rules. Fusion mechanisms considered combinations of characteristic methods in different categories and were implemented by support vector regression and neural networks to yield integrated methods. In siPRED, the prediction of siRNA efficacy through integrated methods was better than through any method that utilized only a single method. Moreover, the weighting of each characteristic method in the context of integrated methods was established by genetic algorithms so that the effect of each characteristic method could be revealed. Using a validation dataset, siPRED performed better than other predictive systems that used the scoring method, neural networks, or linear regression. Finally, siPRED can be improved to achieve a correlation coefficient of 0.777 when the threshold