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

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Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

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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%.

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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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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Authors and Affiliations

  1. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Hongjie Wu, Ye Tang, Weizhong Lu, Cheng Chen, Hongmei Huang & Qiming Fu

  2. Jiangsu Key Laboratory of Intelligent Building Energy Efficiency, Suzhou, 215009, Jiangsu, China

    Weizhong Lu & Qiming Fu

Authors
  1. Hongjie Wu
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  2. Ye Tang
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  3. Weizhong Lu
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  4. Cheng Chen
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  5. Hongmei Huang
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  6. Qiming Fu
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Corresponding author

Correspondence to Weizhong Lu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Wu, H., Tang, Y., Lu, W., Chen, C., Huang, H., Fu, Q. (2018). RNA Secondary Structure Prediction Based on Long Short-Term Memory Model. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_59

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

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