Abstract
This work proposes a novel multi-purpose data standardization method inspired by gene-centric clustering approaches. The clustering is performed via template matching of expression profiles employing Dynamic Time Warping (DTW) alignment algorithm to measure the similarity between the profiles. In this way, for each gene profile a cluster consisting of a varying number of neighboring gene profiles (determined by the degree of similarity) is identified to be used in the subsequent standardization phase. The standardized profiles are extracted via a recursive aggregation algorithm, which reduces each cluster of neighboring expression profiles to a singe profile. The proposed data standardization method is validated on gene expression time series data coming from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.
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Boeva, V., Tsiporkova, E. (2010). A Multi-purpose Time Series Data Standardization Method. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_22
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DOI: https://doi.org/10.1007/978-3-642-13428-9_22
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