Book chapter
Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics
Genetic and Evolutionary Computation – GECCO 2004, pp.438-448
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2004
DOI: 10.1007/978-3-540-24854-5_44
Abstract
The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.
Details
- Title: Subtitle
- Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics
- Creators
- Marylyn D RitchieChristopher S CoffeyJason H Moore
- Resource Type
- Book chapter
- Publication Details
- Genetic and Evolutionary Computation – GECCO 2004, pp.438-448
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-540-24854-5_44
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2004
- Academic Unit
- Biostatistics
- Record Identifier
- 9984214839902771
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