IDEAS home Printed from https://ideas.repec.org/a/adp/jbboaj/v1y2017i2p33-39.html
   My bibliography  Save this article

Feature Selection for Cancer Classification Using Microarray Gene Expression Data

Author

Listed:
  • Wenyan Zhong

    (Department of Mathematics and Statistics, University of Calgary, Canada)

  • Jingjing Wu

    (Department of Mathematics and Statistics, University of Calgary, Canada)

Abstract

The DNA microarray technology enables us to measure the expression levels of thousands of genes simultaneously, providing great chance for cancer diagnosis and prognosis. The number of genes often exceeds tens of thousands, whereas the number of subjects available is often no more than a hundred. Therefore, it is necessary and important to perform gene selection for classification purpose. A good subset of discriminative genes can improve prediction accuracy of classifiers and save computational cost with reduced dimension of data.

Suggested Citation

  • Wenyan Zhong & Jingjing Wu, 2017. "Feature Selection for Cancer Classification Using Microarray Gene Expression Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(2), pages 33-39, April.
    • Handle: RePEc:adp:jbboaj:v:1:y:2017:i:2:p:33-39
    DOI: 10.19080/BBOAJ.2017.01.555557
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/bboaj/pdf/BBOAJ.MS.ID.555557.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/bboaj/BBOAJ.MS.ID.555557.php
    Download Restriction: no
    File URL: https://libkey.io/10.19080/BBOAJ.2017.01.555557?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
    ---><---

    References listed on IDEAS

    as
    1. Shim, Jooyong & Sohn, Insuk & Kim, Sujong & Lee, Jae Won & Green, Paul E. & Hwang, Changha, 2009. "Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1736-1742, March.
    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. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    2. Insuk Sohn & Jooyong Shim & Changha Hwang & Sujong Kim & Jae Won Lee, 2014. "Transcription factor-binding site identification and gene classification via fusion of the supervised-weighted discrete kernel clustering and support vector machine," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 573-581, March.
    3. Ramos, Sandra & Amaral Turkman, Antónia & Antunes, Marília, 2010. "Bayesian classification for bivariate normal gene expression," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2012-2020, August.

    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:adp:jbboaj:v:1:y:2017:i:2:p:33-39. 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: Robert Thomas (email available below). General contact details of provider: .

    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.