IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-031-31654-8_22.html
   My bibliography  Save this book chapter

Generating Genomic Maps of Z-DNA with the Transformer Algorithm

In: Data Analysis and Optimization

Author

Listed:
  • Dmitry Umerenkov

    (Sber Artificial Intelligence Lab)

  • Vladimir Kokh

    (Sber Artificial Intelligence Lab)

  • Alan Herbert

    (HSE University
    InsideOutBio)

  • Maria Poptsova

    (HSE University)

Abstract

Z-DNA and Z-RNA were shown to play an important role in various processes of genome functioning acting as flipons that launch or suppress genetic programs. Genome-wide experimental detection of Z-DNA remains a challenge due to dynamic nature of its formation. Recently we developed a deep learning approach DeepZ, based on CNN and RNN architectures, that predicts Z-DNA regions using additional information from omics data collected from different cell types. Here we took advantage of the transformer algorithm that trains attention maps to improve classifier performance. We started with pretrained DNABERT models and fine-tuned their performance by training with experimental Z-DNA regions from mouse and human genome wide studies. The resulting DNABERT-Z outperformed DeepZ. We demonstrated that DNABERT-Z finetuned on human data sets also generalizes to predict Z-DNA sites in mouse genome.

Suggested Citation

  • Dmitry Umerenkov & Vladimir Kokh & Alan Herbert & Maria Poptsova, 2023. "Generating Genomic Maps of Z-DNA with the Transformer Algorithm," Springer Optimization and Its Applications, in: Boris Goldengorin & Sergei Kuznetsov (ed.), Data Analysis and Optimization, pages 363-376, Springer.
  • Handle: RePEc:spr:spochp:978-3-031-31654-8_22
    DOI: 10.1007/978-3-031-31654-8_22
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:spochp:978-3-031-31654-8_22. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.