Deep Multiple Kernel Learning for Prediction of MicroRNA Precursors

Abstract

MicroRNAs are a group of noncoding RNAs that are about 20–24 nucleotides in length. They are involved in the physiological processes of many diseases and regulate transcriptional and post-transcriptional gene expression. Therefore, the prediction of microRNAs is of great significance for basic biological research and disease treatment. MicroRNA precursors are the necessary stage of microRNA formation. RBF kernel support vector machines (RBF-SVMs) and shallow multiple kernel support vector machines (MK-SVMs) are often used in microRNA precursors prediction. However, the RBF-SVMs could not represent the richer sample features, and the MK-SVMs just use a simply convex combination of few base kernels. This paper proposed a localized multiple kernel learning model with a nonlinear synthetic kernel (LMKL-D). The nonlinear synthetic kernel was trained by a three-layer deep multiple kernel learning model. The LMKL-D model was tested on 2241 pre-microRNAs and 8494 pseudo hairpin sequences. The experiments showed that the LMKL-D model achieved 93.06% sensitivity, 99.27% specificity, and 98.03% accuracy on the test set. The results showed that the LMKL-D model can increase the complexity of kernels and better predict microRNA precursors. Our LMKL-D model can better predict microRNA precursors compared with the existing methods in specificity and accuracy. The LMKL-D model provides a reference for further validation of potential microRNA precursors.