Abstract
RNA secondary structure prediction is an important issue in structural bioinformatics. The difficulty of RNA secondary structure prediction with pseudoknot is increased due to complex structure of the pseudoknot. Traditional machine learning methods, such as support vector machine, markov model and neural network, have been tried and their prediction accuracy are also increasing. The RNA secondary structure prediction problem is transferred into the classification problem of base in the sequence to reduce computational complexity to a certain extent. A model based on LSTM deep recurrent neural network is proposed for RNA secondary structure prediction. Subsequently, comparative experiments were conducted on the authoritative data set RNA STRAND containing 1488 RNA sequences with pseudoknot. The experimental results show that the SEN and PPV of this method are higher than the other two typical methods by 1% and 11%.
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