IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4150106.html
   My bibliography  Save this article

DNA Sequence Specificity Prediction Algorithm Based on Artificial Intelligence

Author

Listed:
  • Xiandun Zhai
  • Adilai Tuerxun
  • Lianhui Li

Abstract

DNA sequence specificity refers to the ability of DNA sequences to bind specific proteins. These proteins play a central role in gene regulation such as transcription and alternative splicing. Obtaining DNA sequence specificity is very important for establishing the regulatory model of the biological system and identifying pathogenic variants. Motifs are sequence patterns shared by fragments of DNA sequences that bind to specific proteins. At present, some motif mining algorithms have been proposed, which perform well under the condition of given motif length. This research is based on deep learning. As for the description of motif level, this paper constructs an AI based method to predict the length of the motif. The experimental results show that the prediction accuracy on the test set is more than 90%.

Suggested Citation

  • Xiandun Zhai & Adilai Tuerxun & Lianhui Li, 2022. "DNA Sequence Specificity Prediction Algorithm Based on Artificial Intelligence," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, October.
    • Handle: RePEc:hin:jnlmpe:4150106
    DOI: 10.1155/2022/4150106
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/4150106.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/4150106.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/4150106?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
    ---><---

    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:hin:jnlmpe:4150106. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.