Complex immunogenetic associations of disease involving a large number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling.
The authors evaluated the performance of feed-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 achieved 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. (...)
Mots-clés Pascal : SIDA, Virose, Infection, Virus immunodéficience humaine, Lentivirus, Retroviridae, Virus, Evaluation, Augmentation, Diffusion, Effet biologique, Système HLA, Système histocompatibilité majeur, Réseau neuronal, Modélisation, Immunogénétique, Epidémiologie, Homme, Immunopathologie, Immunodéficit, Biomathématique
Mots-clés Pascal anglais : AIDS, Viral disease, Infection, Human immunodeficiency virus, Lentivirus, Retroviridae, Virus, Evaluation, Increase, Diffusion, Biological effect, HLA-System, Major histocompatibility system, Neural network, Modeling, Immunogenetics, Epidemiology, Human, Immunopathology, Immune deficiency, Biomathematics
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0206711
Code Inist : 002B05C02D. Création : 11/09/1998.