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.