EMDS: predicting essential miRNAs based on deep learning and sequences

MicroRNAs (miRNAs) as small 19- to 24-nucleotide noncoding RNAs play crucial roles in some key biological progress associated with human diseases. Therefore, identifying the essentiality of miRNAs is important to systematically understand the pathogenic mechanism of diseases. There are some computational methods have been developed to predict essential miRNAs because traditional biological experiments are both time- and labor-consuming. However, these computational methods only used the statistical feature and structural feature of miRNA sequences. The timing characteristics of sequences also should be considered to improve the prediction performance. In addition, the capability deep learning model is well-known. Therefore, in this study, we present a computational method (called EMDS) to predict essential miRNAs. EMDS takes not only the statistical and structural features of sequences but also the subsequence features based on the time characteristics of sequences and Convolutional Neural Networks (CNN). Furthermore, considering that the successful applications of attention mechanism and the subsequence in a miRNA sequence are important, we use a neural attention mechanism to obtain subsequence features of miRNAs. Finally, we integrate the statistical features and structural features, subsequence features as final miRNA features which is inputted into Light Gradient Boosting Machine (LGBM) to predict essential miRNAs. We evaluate the prediction performance of our method by the 5-fold cross validation. We also compare EMDS with other four competing methods which include PESM, miES, Gaussian Naive Bayes (Gaus_NB) and Support Vector Machine (SVM) by performing same cross validation experiments. The results show that EMDS achieves better prediction performance in terms of AUC (EMDS:0.9335, PESM:0.9117, miES:0.8837, Gaus_NB:0.8720, SVM:0.8571). It also illustrates that our method can effectively predict the essential miRNAs.