Predicting RNA secondary structure based on machine learning and genetic algorithm

In recent years, RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a genetic algorithm method to improve the accuracy with large-scale RNA sequence and structure data...