Effective small interfering RNA design based on convolutional neural network

In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accuracy is not so satisfactory. In this paper, to explore the effect of different motifs on gene silencing and further improve the prediction accuracy, we developed a new powerful predictor by using a deep learning algorithm - Convolutional Neural Network (CNN). The comparison results showed that the Pearson Correlation Coefficient (PCC) of our model is 0.717, which is 13.81%, 16.78% and 5.91% higher than Biopredsi, i-Score, ThermoComposition21 and DSIR. In addition, the area under the ROC curve (AUC) of our model is 0.894, which is 10.10%, 12.59% and 7.07% higher than those four algorithms. The results show that our model is stable and efficient to predict siRNA silencing efficacy.