Support vector machine for prediction of siRNA silencing efficacy

In order to assist the design of short interfering ribonucleic acids (siRNA), 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference (RNAi) effect is analyzed by a support vector machine (SVM) based algorithm relied on a base-base correlation (BBC) feature. The results show that the proposed algorithm has the highest area under curve (AUC) value (0.73) of the receive operating characteristic (ROC) curve and the greatest r value (0.43) of the Pearson's correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.