IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0245982.html
   My bibliography  Save this article

OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

Author

Listed:
  • Yawu Zhao
  • Yihui Liu

Abstract

Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.

Suggested Citation

  • Yawu Zhao & Yihui Liu, 2021. "OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-14, February.
    • Handle: RePEc:plo:pone00:0245982
    DOI: 10.1371/journal.pone.0245982
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245982
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0245982&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0245982?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jorge Brieva & Hiram Ponce & Ernesto Moya-Albor, 2023. "Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture," Mathematics, MDPI, vol. 11(3), pages 1-23, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0245982. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.