Computational Optimization and Applications

, Volume 38, Issue 2, pp 281–298 | Cite as

Modeling sequence evolution with kernel methods

  • Margherita Bresco
  • Marco Turchi
  • Tijl De Bie
  • Nello CristianiniEmail author


We model the evolution of biological and linguistic sequences by comparing their statistical properties. This comparison is performed by means of efficiently computable kernel functions, that take two sequences as an input and return a measure of statistical similarity between them. We show how the use of such kernels allows to reconstruct the phylogenetic trees of primates based on the mitochondrial DNA (mtDNA) of existing animals, and the phylogenetic tree of Indo-European and other languages based on sample documents from existing languages.

Kernel methods provide a convenient framework for many pattern analysis tasks, and recent advances have been focused on efficient methods for sequence comparison and analysis. While a large toolbox of algorithms has been developed to analyze data by using kernels, in this paper we demonstrate their use in combination with standard phylogenetic reconstruction algorithms and visualization methods.


Feature Space Leaf Node Kernel Method Kernel Matrix Gorilla Gorilla 
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 Science+Business Media, LLC 2007

Authors and Affiliations

  • Margherita Bresco
    • 1
  • Marco Turchi
    • 2
  • Tijl De Bie
    • 3
  • Nello Cristianini
    • 4
    Email author
  1. 1.Department of Mathematics and InformaticsUniversity of SalernoSalernoItaly
  2. 2.Department of Information EngineeringUniversity of SienaSienaItaly
  3. 3.ECS, ISIS Research GroupUniversity of SouthamptonSouthamptonUK
  4. 4.Department of StatisticsUniversity of CaliforniaDavisUSA

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