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Multispecies Gene Entropy Estimation, a Data Mining Approach

  • Xiaoxu Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)

Abstract

This paper presents a data mining approach to estimate multispecies gene entropy by using a self-organizing map (SOM) to mine a homologous gene set. The gene distribution function for each gene in the feature space is approximated by its probability distribution in the feature space. The phylogenetic applications of the multispecies gene entropy are investigated in an example of inferring the species phylogeny of eight yeast species. It is found that genes with the nearest K-L distances to the minimum entropy gene are more likely to be phylogenetically informative. The K-L distances of genes are strongly correlated with the spectral radiuses of their identity percentage matrices. The images of identity percentage matrices of the genes with small K-L distances to the minimum entropy gene are more similar to the image of the minimum entropy gene in their frequency domains after fast Fourier transforms (FFT) than the images of those genes with large K-L distances to the minimum entropy gene. Finally, a K-L distance based gene concatenation approach under gene clustering is proposed to infer species phylogenies robustly and systematically.

Keywords

Sequence Space Gene Entropy Informative Gene Data Mining Approach Good Match Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoxu Han
    • 1
  1. 1.Department of Mathematics and Bioinformatics ProgramEastern Michigan UniversityYpsilantiUSA

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