IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v387y2008i16p4223-4247.html
   My bibliography  Save this article

Studies of spectral properties of short genes using the wavelet subspace Hilbert–Huang transform (WSHHT)

Author

Listed:
  • Jiang, Rong
  • Yan, Hong

Abstract

This paper presents a new algorithm for the analysis of spectral properties of short genes using the wavelet transform and the Hilbert–Huang transform (HHT). A wavelet subspace algorithm combined with the empirical mode decomposition (EMD) is introduced to create subdivided intrinsic mode functions (IMFs) and a cross-correlation analysis is applied to remove pseudo-spectral components. Experiments are carried out on DNA sequences with the double-base (DB) curve representation and the results show that the signal-to-noise ratio of buried signals can be enhanced using the proposed method, yielding significant patterns that are rarely observed with conventional methods. The wavelet subspace Hilbert–Huang transform (WSHHT) algorithm is able to correctly identify spectral patterns of very short genes (below 70 bp) in DNA sequences.

Suggested Citation

  • Jiang, Rong & Yan, Hong, 2008. "Studies of spectral properties of short genes using the wavelet subspace Hilbert–Huang transform (WSHHT)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4223-4247.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:16:p:4223-4247
    DOI: 10.1016/j.physa.2008.02.076
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108002409
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2008.02.076?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. Sofia C. Olhede & Andrew T. Walden, 2004. "'Analytic' wavelet thresholding," Biometrika, Biometrika Trust, vol. 91(4), pages 955-973, December.
    2. Li, Helong & Yang, Lihua & Huang, Daren, 2005. "The study of the intermittency test filtering character of Hilbert–Huang transform," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 70(1), pages 22-32.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaolei Zhang & Weijun Pan, 2019. "Exon prediction based on multiscale products of a genomic-inspired multiscale bilateral filtering," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.
    2. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    3. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2016. "Cross-correlation analysis of stock markets using EMD and EEMD," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 82-90.

    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. Fryzlewicz, Piotr, 2007. "Bivariate hard thresholding in wavelet function estimation," LSE Research Online Documents on Economics 25219, London School of Economics and Political Science, LSE Library.

    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:eee:phsmap:v:387:y:2008:i:16:p:4223-4247. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.