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A combinatorics-based data-mining approach to time-series microarray alignment

Abstract : One of the biological issues aiming at understanding bovine embryo development implies the analysis of proliferation and differentiation processes. An easy way to do so is to use published data to collect information about genes interacting with a target gene of interest from which we can extract pieces of information from the literature. Using published data from other species (mouse, human) we used a double-step classical clustering approach. First step runs a k-mean clustering for each chip individually. Second step runs a fuzzy consensus clustering to merge a few clusters (i.e. megaclusters) between microarrays. Hence we make temporal gene profiles (i.e matrix) based on gene expression of megaclusters using the symbolic time property of simultaneity and precedence. Finally with the help of a Jaccard coefficient between temporal gene profiles across species, we extract a list of genes revealing a similarity with a target gene of interest. Depending on the species or target gene, this list of genes differed in size and content, thus highlighting the interest of such cross- species comparisons to gain insights from different literature contexts.
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  • HAL Id : hal-02660788, version 1
  • PRODINRA : 29740



Nicolas Turenne, Isabelle Hue. A combinatorics-based data-mining approach to time-series microarray alignment. Informacionnyj Vestnik VOGiS, 2009, 13 (1), pp.109-113. ⟨hal-02660788⟩



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