IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v14y2015i2p221-224n4.html
   My bibliography  Save this article

GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net

Author

Listed:
  • Vlassis Nikos

    (Adobe Research, Systems Technology Lab/Imagination Lab, 345 Park Avenue, San Jose, CA 95110, USA)

  • Glaab Enrico

    (University of Luxembourg, Luxembourg Centre for Systems Biomedicine, 7, avenue des Hauts Fourneaux, Esch-sue-Alzette 4362, Luxembourg)

Abstract

Complex diseases are often characterized by coordinated expression alterations of genes and proteins which are grouped together in a molecular network. Identifying such interconnected and jointly altered gene/protein groups from functional omics data and a given molecular interaction network is a key challenge in bioinformatics. We describe GenePEN, a penalized logistic regression approach for sample classification via convex optimization, using a newly designed Pairwise Elastic Net penalty that favors the selection of discriminative genes/proteins according to their connectedness in a molecular interaction graph. An efficient implementation of the method finds provably optimal solutions on high-dimensional omics data in a few seconds and is freely available at http://lcsb-portal.uni.lu/bioinformatics.

Suggested Citation

  • Vlassis Nikos & Glaab Enrico, 2015. "GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 221-224, April.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:2:p:221-224:n:4
    DOI: 10.1515/sagmb-2014-0045
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2014-0045
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2014-0045?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.

    References listed on IDEAS

    as
    1. Enrico Glaab & Jaume Bacardit & Jonathan M Garibaldi & Natalio Krasnogor, 2012. "Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kang, Xiaoning & Wang, Mingqiu, 2021. "Ensemble sparse estimation of covariance structure for exploring genetic disease data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Patrick Murigu Kamau Njage & Clementine Henri & Pimlapas Leekitcharoenphon & Michel‐Yves Mistou & Rene S. Hendriksen & Tine Hald, 2019. "Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1397-1413, June.
    3. Gao, Zhenguo & Wang, Xinye & Kang, Xiaoning, 2023. "Ensemble LDA via the modified Cholesky decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    4. Makoto Aoshima & Kazuyoshi Yata, 2019. "Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 473-503, June.

    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:bpj:sagmbi:v:14:y:2015:i:2:p:221-224:n:4. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.