Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis

  • Jason H. Moore
  • Bill C. White
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


Human genetics is undergoing an information explosion. The availability of chip-based technology facilitates the measurement of thousands of DNA sequence variation from across the human genome. The challenge is to sift through these high-dimensional datasets to identify combinations of interacting DNA sequence variations that are predictive of common diseases. The goal of this paper was to develop and evaluate a genetic programming (GP) approach for attribute selection and modeling that uses expert knowledge such as Tuned ReliefF (TuRF) scores during selection to ensure trees with good building blocks are recombined and reproduced. We show here that using expert knowledge to select trees performs as well as a multiobjective fitness function but requires only a tenth of the population size. This study demonstrates that GP may be a useful computational discovery tool in this domain.


Genetic Programming Expert Knowledge Multifactor Dimensionality Reduction Genetic Programming Approach International HapMap Consortium 
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

  • Jason H. Moore
    • 1
  • Bill C. White
    • 1
  1. 1.Computational Genetics Laboratory, Department of GeneticsDartmouth Medical SchoolLebanonUSA

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