C4.5: programs for machine learning
C4.5: programs for machine learning
Building neural networks
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
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
Genetic programming neural networks: A powerful bioinformatics tool for human genetics
Applied Soft Computing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Comparison of neural network optimization approaches for studies of human genetics
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Applying grammatical evolution to evolve neural networks (GENN) has been increasing used in genetic epidemiology to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional data. GENN approaches have previously been shown to be highly successful in a range of simulated and real case-control studies, and has recently been applied to quantitative traits. In the current study, we evaluate the potential of an application of GENN to quantitative traits (QTGENN) to a range of simulated genetic models. We demonstrate the power of the approach, and compare this power to more traditional linear regression analysis approaches. We find that the QTGENN approach has relatively high power to detect both single-locus models as well as several completely epistatic two-locus models, and favorably compares to the regression methods.