Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan.
PLoS One. 2011;6(11):e27602. doi: 10.1371/journal.pone.0027602. Epub 2011 Nov 10.
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 of the whole stacking energy is ≥-34.6 kcal/mol. siPRED is freely available on the web at http://predictor.nchu.edu.tw/siPRED.
小干扰 RNA（siRNA）已被广泛用于诱导细胞中的基因沉默。为了预测 siRNA 抑制其靶 mRNA 的功效，我们开发了一个两层系统 siPRED，它基于第一层的各种特征方法和第二层的融合机制。特征方法是通过支持向量回归从三个类别（序列、特征和规则）的特征构建的。融合机制考虑了不同类别中特征方法的组合，并通过支持向量回归和神经网络实现，以生成综合方法。在 siPRED 中，通过综合方法预测 siRNA 的功效优于仅使用单一方法的方法。此外，通过遗传算法建立了综合方法中每个特征方法的权重，以揭示每个特征方法的效果。使用验证数据集，siPRED 的性能优于使用评分方法、神经网络或线性回归的其他预测系统。最后，当全堆叠能的阈值≥-34.6 kcal/mol 时，siPRED 可以通过改进达到 0.777 的相关系数。siPRED 可在 http://predictor.nchu.edu.tw/siPRED 上免费获得。
