期刊
APPLIED SOFT COMPUTING
卷 7, 期 1, 页码 471-479出版社
ELSEVIER
DOI: 10.1016/j.asoc.2006.01.013
关键词
nueral networks; genetic programming; bioinformatics; epistasis; gene-gene interactions
资金
- NHLBI NIH HHS [R01 HL065234, R01 HL065234-01, U19 HL065962, U01 HL065962, U01 HL065962-01A1] Funding Source: Medline
- NIAID NIH HHS [R01 AI059694, R01 AI059694-01] Funding Source: Medline
- NIA NIH HHS [R01 AG020135-01, R01 AG020135, R01 AG019085, R01 AG019085-01A1] Funding Source: Medline
- NICHD NIH HHS [R01 HD047447, R01 HD047447-01] Funding Source: Medline
- NIGMS NIH HHS [P01 GM031304-17, P01 GM031304] Funding Source: Medline
- NLM NIH HHS [T15 LM007450-06, T15 LM007450] Funding Source: Medline
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH &HUMAN DEVELOPMENT [R01HD047447] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [U19HL065962, U01HL065962, R01HL065234] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [R01AI059694] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P01GM031304] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [R01AG020135, R01AG019085] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [T15LM007450] Funding Source: NIH RePORTER
The identification of genes that influence the risk of common, complex disease 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 genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease. (C) 2006 Elsevier B.V. All rights reserved.
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