Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
An appraisal of a decision tree approach to image classification
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
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A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such interactive models present an important analytical challenge, requiring that methods perform both variable selection and statistical modeling to generate testable genetic model hypotheses. Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interactive effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. Currently, we introduce the Grammatical Evolution Decision Trees (GEDT) method, and demonstrate that GEDT has power to detect interactive models in a range of simulated data, revealing GEDT to be a promising new approach for human genetics.