An Optimized Technique for RNA Prediction Based on Neural Network

Pathway reconstruction, which remains a primary goal for many investigations, requires accurate inference of gene interactions and causality. Non-coding RNA (ncRNA) is studied because it has significant regulatory role in plant animal life activities, but interacting micro-RNA (miRNA) long non-coding (lncRNA) are more important. Their not only aid the in-depth research genes’ biological roles, also bring new ideas illness detection therapy, as well genetic breeding. Biological investigations classical machine learning methods now used to predict miRNA-lncRNA interactions. Because identification expensive time-consuming, too much manual intervention, feature extraction process difficult. This presents deep model that combines advantages convolutional neural networks (CNN) bidirectional short-term memory (Bi-LSTM). It takes into account connection information between sequences incorporates contextual data, thoroughly extracts sequence data’s features. On corn data set, cross-checking evaluate model’s performance, compared learning. To acquire superior classification effect, proposed strategy was single model. Additionally, potato wheat sets were utilized model, with accuracy rates 95% 93%, respectively, indicating had strong generalization capacity.