An introduction to genetic algorithms
An introduction to genetic algorithms
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Artificial Neural Networks
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
Can neural network constraints in GP provide power to detect genes associated with human disease?
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
IEEE Transactions on Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The power of quantitative grammatical evolution neural networks to detect gene-gene interactions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. The preponderance of gene-gene and gene-environment interactions comprising the genetic architecture of common diseases presents a difficult challenge. To address this, novel computational approaches have been applied to studies of human disease. These novel approaches seek to capture the complexity inherent in common diseases. Previously, we developed a genetic programming neural network (GPNN) to optimize network architecture for the detection of disease susceptibility genes in association studies. While GPNN was a successful endeavor, we wanted to address the limitations in its flexibility and ease of development. To this end, we developed a grammatical evolution neural network (GENN) approach that accounts for the drawbacks of GPNN. In this study we show that this new method has high power to detect gene-gene interactions in simulated data. We also compare the performance of GENN to GPNN, a traditional back-propagation neural network (BPNN) and a random search algorithm. GENN outperforms both BPNN and the random search, and performs at least as well as GPNN. This study demonstrates the utility of using GE to evolve NN in studies of complex human disease.