IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v15y2023i3d10.1007_s12561-023-09367-9.html
   My bibliography  Save this article

Integrative Structural Learning of Mixed Graphical Models via Pseudo-likelihood

Author

Listed:
  • Qingyang Liu

    (University of Connecticut)

  • Yuping Zhang

    (University of Connecticut)

Abstract

Markov random field is a common tool to characterize interactions among a fixed collection of variables. In recent biomedical research, there arise new concerns about the discovery of regulatory and co-expression relationships among different types of features across multiple biological classes. Consequently, we propose a data integration framework to jointly learn multiple mixed graphical models simultaneously. To address the common asymmetry problem in neighborhood selection, we construct a new estimator using regularized pseudo-likelihood, which produces symmetric and consistent estimates of network topologies. We demonstrate the practical merits of our method through learning synthetic networks as well as constructing gene regulatory networks from TCGA data.

Suggested Citation

  • Qingyang Liu & Yuping Zhang, 2023. "Integrative Structural Learning of Mixed Graphical Models via Pseudo-likelihood," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(3), pages 562-582, December.
    • Handle: RePEc:spr:stabio:v:15:y:2023:i:3:d:10.1007_s12561-023-09367-9
    DOI: 10.1007/s12561-023-09367-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-023-09367-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-023-09367-9?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:stabio:v:15:y:2023:i:3:d:10.1007_s12561-023-09367-9. 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.