Similarity Matches of Gene Expression Data Based on Wavelet Transform

Authors:
Mong-Shu Lee;Mu-Yen Chen;Li-Yu Liu
Affiliations:
Department of Computer Science & Engineering, National Taiwan Ocean University, Keelung, Taiwan R.O.C.;Department of Computer Science & Engineering, National Taiwan Ocean University, Keelung, Taiwan R.O.C.;Department of Computer Science & Engineering, National Taiwan Ocean University, Keelung, Taiwan R.O.C.
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

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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.