Prediction of siRNA functionality using generalized string kernel and support vector machine
- 作者：Teramoto ; Reiji ; Aoki ; Mikio ; Kimura ; Toru ; Kanaoka ; Masaharu
- 关键词：Small interfering RNA ; RNA interference ; String kernel ; Support vector machine ; Functional genomics ; Leave-one-out cross-validation
- 刊名：FEBS Letters
- 出版年：2005
- 期刊代码：70_00145793
- 类别：bio
- 出版时间：May 23, 2005
- 卷：579
- 期：13
- 页码：2878-2882
- 文件大小：160 K
摘要
Small interfering RNAs (siRNAs) are becoming widely used for sequence-specific gene silencing in mammalian cells, but designing an effective siRNA is still a challenging task. In this study, we developed an algorithm for predicting siRNA functionality by using generalized string kernel (GSK) combined with support vector machine (SVM). With GSK, siRNA sequences were represented as vectors in a multi-dimensional feature space according to the numbers of subsequences in each siRNA, and subsequently classified with SVM into effective or ineffective siRNAs. We applied this algorithm to published siRNAs, and could classify effective and ineffective siRNAs with 90.6%, 86.2%accuracy, respectively.