Prediction of Plant lncRNA-Protein Interactions Using Sequence Information Based on Deep Learning

Abstract

Plant long non-coding RNA (lncRNA) plays an important role in many biological processes, mainly through its interaction with RNA binding protein (RBP). To understand the function of lncRNA, a basic step is to determine which proteins are interacted with lncRNA. Therefore, RBP can be predicted by computational approaches. However, the main challenge is that it is difficult to find interaction patterns or primitives. In this study, we propose a method based on sequences to predict plant lncRNA-protein interaction, namely PLRPI uses k-mer frequency feature for RNA and protein, stacked denoising autoencoder and gradient boosting decision tree to learn the hidden interaction between plant lncRNAs and proteins sequences. The experimental results show that PLRPI achieves good performance on the test datasets ATH948 and ZEA22133 based on lncRNA-protein interaction of Arabidopsis thaliana and Zea mays. Our method gets an accuracy of 90.4% on ATH948 and 82.6% on ZEA22133. PLRPI is also superior to other methods in some public RNA-protein interaction datasets. The result shows PLRPI has strong generalization ability and high robustness. It is an effective model for predicting plant lncRNA-protein interactions.