MVRM: A Hybrid Approach to Predict siRNA Efficacy

Abstract

The discovery of RNA interference (RNAi) leads to design novel drugs for different diseases. Selecting short interfering RNAs (siRNAs) that can knockdown target genes efficiently is one of the key tasks in studying RNAi. A number of predictive models have been proposed to predict knockdown efficacy of siRNAs, however, their performance is still far from the expectation. This work aims to develop a predictive model to enhance siRNA knockdown efficacy prediction. The key idea is to combine both the rule -- based and the model -- based approaches. To this end, views of siRNAs that integrate available siRNA design rules are first learned using an adaptive Fuzzy C Means (FCM) algorithm. The learned views and other properties of siRNAs are combined to final representations of siRNAs. The elastic net regression method is employed to learn a predictive model from these final representations. Experiments on benchmark datasets showed that the proposed method achieved stable and accurate results in comparison with other methods.