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RNA Secondary Structure Prediction Based on Long Short-Term Memory Model

  • Hongjie Wu
  • Ye Tang
  • Weizhong Lu
  • Cheng Chen
  • Hongmei Huang
  • Qiming Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

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%.

Keywords

RNA secondary structure prediction Recurrent neural network Pseudoknots Classification 

Notes

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (61772357, 61502329, 61672371), Jiangsu 333 talent project and top six talent peak project (DZXX-010), Suzhou Foresight Research Project (SYG201704, SNG201610) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0680).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hongjie Wu
    • 1
  • Ye Tang
    • 1
  • Weizhong Lu
    • 1
    • 2
  • Cheng Chen
    • 1
  • Hongmei Huang
    • 1
  • Qiming Fu
    • 1
    • 2
  1. 1.School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
  2. 2.Jiangsu Key Laboratory of Intelligent Building Energy EfficiencySuzhouChina

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