title: Prediction of siRNA functionality using generalized string kernel and support vector machine.
abstract: 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.
authors.affiliation: Genomic Science Laboratories, Sumitomo Pharmaceuticals Co., Ltd., Osaka, Japan.
authors.forename: Reiji
authors.initials: R
authors.lastname: Teramoto
authors.forename: Mikio
authors.initials: M
authors.lastname: Aoki
authors.forename: Toru
authors.initials: T
authors.lastname: Kimura
authors.forename: Masaharu
authors.initials: M
authors.lastname: Kanaoka
mesh_terms: D000465:Algorithms; D020868:Gene Silencing; D005822:Genetic Vectors; D034741:RNA, Small Interfering / Q000502:physiology*
chemical_list: D034741:RNA, Small Interfering