title: SiRNA silencing efficacy prediction using the RNA string kernel.
abstract: While most existing string kernels are developed for general purpose sequences and have been applied to text and protein classifications, the RNA string kernel is particularly designed to model mismatches, G-U wobbles, and bulges of RNA biology. We adapt the RNA kernel to compute the similarity of the short interfering RNAs (siRNAs), initiators of RNA interference, and use it in support vector regression to predict the siRNA silencing efficacy treated as a continuous variable. Empirical results on biological data sets demonstrate that the RNA string kernel performed favourably. In addition, it is simple to implement and fast to compute.
authors.affiliation: Pathwork Diagnostics Inc., 1196 Borregas Ave., Sunnyvale CA, 94089, USA. sqiu@pathworkdx.com
authors.forename: Shibin
authors.initials: S
authors.lastname: Qiu
authors.forename: Terran
authors.initials: T
authors.lastname: Lane
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods; D020868:Gene Silencing; D006801:Humans; D034741:RNA, Small Interfering / Q000378:metabolism*; D012044:Regression Analysis; D017423:Sequence Analysis, RNA
chemical_list: D034741:RNA, Small Interfering