American Journal of Epidemiology Vol. 147, No. 5: 464-471
Copyright © 1998 by The Johns Hopkins University School of Hygiene and Public Health
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Use of Neural Networks to Model Complex Immunogenetic Associations of Disease: Human Leukocyte Antigen Impact on the Progression of Human Immunodeficiency Virus Infection
1HIV Research Branch, Division of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health Bethesda, MD
2Division of Computer Research and Technology, National Institutes of Health Bethesda, MD
3Viral Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health Bethesda, MD
4Department of Epidemiolgoy, University of Alabama School of Public Health Birmingham, AL
Reprint requests to Dr. John P. A. Ioannidis, HIV Research Branch, Division of AIDS, National Institute of Allergy and Infectious Diseases, NIH, Solar Building Room 2C31, 6003 Executive Blvd., Bethesda, MD 20892.
Complex immunogenetic associations of disease involving a learge number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling. The authors evaluated the performance of freed-forward backpropagation neural networks in predicting rapid progression to acquired immunodeficiency syndrome (AIDS) for patients with human immunodeficiency virus (HIV) infection on the basis of major histocompatibility complex variables. Networks were trained on data from patients from the Multicenter AIDS Cohort Study (n = 139) and then validated on patients from the DC Gay cohort (n = 102). The outcome of interest was rapid disease progression, defined as progression to AIDS in <6 years from seroconversion. Human leukocyte antigen (HLA) variables were selected as network inputs with multivariate regression and a previously described algorithm selecting markers with extreme point estimates for progression risk. Network performance was compared with that of logistic regression. Networks with 15 HLA inputs and a single hidden layer of five nodes acheived a sensitivity of 87.5% and specificity of 95.6% in the training set, vs. 77.0% and 76.9% respectively, achieved by logistic regression. When validated on the DC Gay cohort, networks averaged a sensitivity of 59.1% and specificity of 74.3%, vs. 53.1% and 61.4%, respectively, for logistic regression. Neural networks offer further support to the notion that HIV disease progrssion may be dependent on complex interactions between different class I and clase II alleles and transporters associated with antigen processing variants. The effect in the current models is of moderate magnitude, and more data as well as other host and pathogen variables may need to be considered to improve the performance of the models. Artificial intelligence methods may complement linear statistical methods for evaluating immunogenetic associations of disease. Am J Epidemiol 1998; 147:464–71.
acquired immunodeficiency syndrome; HIV; HLA antigens; logistic models; major histocompatibility complex; neural networks (computer)
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