IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0067995.html
   My bibliography  Save this article

SMETANA: Accurate and Scalable Algorithm for Probabilistic Alignment of Large-Scale Biological Networks

Author

Listed:
  • Sayed Mohammad Ebrahim Sahraeian
  • Byung-Jun Yoon

Abstract

In this paper we introduce an efficient algorithm for alignment of multiple large-scale biological networks. In this scheme, we first compute a probabilistic similarity measure between nodes that belong to different networks using a semi-Markov random walk model. The estimated probabilities are further enhanced by incorporating the local and the cross-species network similarity information through the use of two different types of probabilistic consistency transformations. The transformed alignment probabilities are used to predict the alignment of multiple networks based on a greedy approach. We demonstrate that the proposed algorithm, called SMETANA, outperforms many state-of-the-art network alignment techniques, in terms of computational efficiency, alignment accuracy, and scalability. Our experiments show that SMETANA can easily align tens of genome-scale networks with thousands of nodes on a personal computer without any difficulty. The source code of SMETANA is available upon request. The source code of SMETANA can be downloaded from http://www.ece.tamu.edu/~bjyoon/SMETANA/.

Suggested Citation

  • Sayed Mohammad Ebrahim Sahraeian & Byung-Jun Yoon, 2013. "SMETANA: Accurate and Scalable Algorithm for Probabilistic Alignment of Large-Scale Biological Networks," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
  • Handle: RePEc:plo:pone00:0067995
    DOI: 10.1371/journal.pone.0067995
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067995
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0067995&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0067995?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. Hyun-Myung Woo & Hyundoo Jeong & Byung-Jun Yoon, 2020. "NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-20, January.
    2. Shawn Gu & Tijana Milenković, 2020. "Data-driven network alignment," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-30, July.

    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:pone00:0067995. 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.