Building neural networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Generating linkage disequilibrium patterns in data simulations using genomeSIMLA
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Comparison of neural network optimization approaches for studies of human genetics
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
IEEE Transactions on Evolutionary Computation
Incorporating domain knowledge into evolutionary computing for discovering gene-gene interaction
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
The power of quantitative grammatical evolution neural networks to detect gene-gene interactions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
ATHENA optimization: the effect of initial parameter settings across different genetic models
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
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Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that amongst the small main effects of each single gene on disease susceptibility, there are non-linear, gene-gene interactions that can be difficult for traditional, parametric analyses to detect. In addition, exhaustively searching all multi-locus combinations has proved computationally impractical. Novel strategies for analysis have been developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the greatest influence on detection are: input variable encoding, population size, and parallel computation.