Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases

  • Authors:

  • Jason H. Moore;Peter C. Andrews;Nate Barney;Bill C. White

  • Affiliations:

  • Computational Genetics Labroatory, Department of Genetics, Dartmouth Medical School, Lebanon, NH;Computational Genetics Labroatory, Department of Genetics, Dartmouth Medical School, Lebanon, NH;Computational Genetics Labroatory, Department of Genetics, Dartmouth Medical School, Lebanon, NH;Computational Genetics Labroatory, Department of Genetics, Dartmouth Medical School, Lebanon, NH

  • Venue:
  • EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
  • Year:
  • 2008

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Abstract

An important goal of human genetics is to identify DNA sequence variations that are predictive of susceptibility to common human diseases. This is a classification problem with data consisting of discrete attributes and a binary outcome. A variety of different machine learning methods based on artificial evolution have been developed and applied to modeling the relationship between genotype and phenotype. While artificial evolution approaches show promise, they are far from perfect and are only loosely based on real biological and evolutionary processes. It has recently been suggested that a new paradigm is needed where "artificial evolution" is transformed to "computational evolution" (CE) by incorporating more biological and evolutionary complexity into existing algorithms. It has been proposed that CE systems will be more likely to solve problems of interest to biologists and biomedical researchers. The goal of the present study was to develop and evaluate a prototype CE system for the analysis of human genetics data. We describe here this new open-ended CE system and provide initial results from a simulation study that suggests more complex operators result in better solutions.