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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Sofia C. Olhede & Andrew T. Walden, 2004. "'Analytic' wavelet thresholding," Biometrika, Biometrika Trust, vol. 91(4), pages 955-973, December.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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