- author = "Marylyn D. Ritchie and Bill C. White and Joel S. Parker and Lance W. Hahn and Jason H. Moore",
- title = "Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases",
- journal = "BMC Bioinformatics",
- year = "2003",
- volume = "4",
- number = "28",
- month = "7 " # jul,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.biomedcentral.com/1471-2105/4/28",
-
DOI = "
doi:10.1186/1471-2105-4-28",
- size = "14 pages",
-
abstract = "Background
Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases.
Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.",
- notes = "Cited by New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background Silvana Penco, Massimo Buscema, Maria Cristina Patrosso, Alessandro Marocchi, and Enzo Grossi BMC Bioinformatics. 2008; 9: 254. Published online 2008 May 30. doi:10.1186/1471-2105-9-254. PMCID: PMC2443147",
