Similarity analysis of time series gene expression using dual-tree wavelet transform

This study presents a similarity-determining rnethod 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 fact that Dual-Tree Wavelet Transform (DTWT) can provide 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.