PMID- 9525533
OWN - NLM
STAT- MEDLINE
DCOM- 19980407
LR  - 20191211
IS  - 0002-9262 (Print)
IS  - 0002-9262 (Linking)
VI  - 147
IP  - 5
DP  - 1998 Mar 1
TI  - Use of neural networks to model complex immunogenetic associations of disease: 
      human leukocyte antigen impact on the progression of human immunodeficiency virus 
      infection.
PG  - 464-71
AB  - 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. Neural 
      networks offer further support to the notion that HIV disease progression may be 
      dependent on complex interactions between different class I and class 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.
FAU - Ioannidis, J P
AU  - Ioannidis JP
AD  - HIV Research Branch, Division of AIDS, National Institute of Allergy and 
      Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
FAU - McQueen, P G
AU  - McQueen PG
FAU - Goedert, J J
AU  - Goedert JJ
FAU - Kaslow, R A
AU  - Kaslow RA
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Am J Epidemiol
JT  - American journal of epidemiology
JID - 7910653
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Acquired Immunodeficiency Syndrome/*immunology/*pathology
MH  - Adult
MH  - Disease Progression
MH  - Forecasting
MH  - HIV Infections/*immunology/*pathology
MH  - HLA Antigens/immunology
MH  - Humans
MH  - Logistic Models
MH  - Major Histocompatibility Complex/*immunology
MH  - Male
MH  - *Neural Networks, Computer
MH  - Prognosis
MH  - Sensitivity and Specificity
EDAT- 1998/04/03 00:00
MHDA- 1998/04/03 00:01
CRDT- 1998/04/03 00:00
PHST- 1998/04/03 00:00 [pubmed]
PHST- 1998/04/03 00:01 [medline]
PHST- 1998/04/03 00:00 [entrez]
AID - 10.1093/oxfordjournals.aje.a009472 [doi]
PST - ppublish
SO  - Am J Epidemiol. 1998 Mar 1;147(5):464-71. doi: 
      10.1093/oxfordjournals.aje.a009472.