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Best-Fit in Linear Time for Non-generative Population Simulation

(Extended Abstract)
  • Niina Haiminen
  • Claude Lebreton
  • Laxmi Parida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8701)

Abstract

Constructing populations with pre-specified characteristics is a fundamental problem in population genetics and other applied areas. We present a novel non-generative approach that deconstructs the desired population into essential local constraints and then builds the output bottom-up. This is achieved using primarily best-fit techniques from discrete methods, which ensures accuracy of the output. Also, the algorithms are fast, i.e., linear, or even sublinear, in the size of the output. The non-generative approach also results in high sensitivity in the algotihms. Since the accuracy and sensitivity of the population simulation is critical to the quality of the output of the applications that use them, we believe that these algorithms will provide a strong foundation to the methods in these studies.

Keywords

Haplotype Block Interference Model Crossover Event International HapMap Project Minimum Allele Frequency 
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 2014

Authors and Affiliations

  • Niina Haiminen
    • 1
  • Claude Lebreton
    • 2
  • Laxmi Parida
    • 1
  1. 1.Computational Biology Center, IBM T. J. Watson ResearchUSA
  2. 2.Limagrain Europe, Centre de Recherche de ChappesFrance

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