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Two-Level ACO for Haplotype Inference Under Pure Parsimony

  • Stefano Benedettini
  • Andrea Roli
  • Luca Di Gaspero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)

Abstract

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information enables researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents.

A notable approach to the problem is to encode it as a combinatorial problem under certain hypotheses (such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. At present, the main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods, which are adequate only for moderate size instances.

In this paper, we present an iterative constructive approach to Haplotype Inference based on ACO and we compare it against a state-of-the-art exact method.

Keywords

Local Search Solution Quality Distinct Haplotype Haplotype Inference Stochastic Local Search 
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 2008

Authors and Affiliations

  • Stefano Benedettini
    • 1
  • Andrea Roli
    • 1
  • Luca Di Gaspero
    • 2
  1. 1.DEIS, Campus of Cesena Alma Mater StudiorumUniversità di BolognaCesenaItaly
  2. 2.DIEGMUniversity of UdineUdineItaly

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