Similarity Matches of Gene Expression Data Based on Wavelet Transform

  • Mong-Shu Lee
  • Mu-Yen Chen
  • Li-Yu Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

This study presents a similarity-determining method for measuring regulatory relationships between pairs of genes from microarray time series data. The proposed similarity metrics are based on a new method to measure structural similarity to compare the quality of images. We make use of the Dual-Tree Wavelet Transform (DTWT) since it provides approximate shift invariance and maintain the structures between pairs of regulation related time series expression data. Despite the simplicity of the presented method, experimental results demonstrate that it enhances the similarity index when tested on known transcriptional regulatory genes.

Keywords

Wavelet transform Time series gene expression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mong-Shu Lee
    • 1
  • Mu-Yen Chen
    • 1
  • Li-Yu Liu
    • 1
  1. 1.Department of Computer Science & EngineeringNational Taiwan Ocean UniversityKeelungTaiwan R.O.C.

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