Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.
PLoS Comput Biol
; 11(4): e1004185, 2015 Apr.
Article
em En
| MEDLINE
| ID: mdl-25874406
ABSTRACT
The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Anticorpos Anti-HIV
/
Infecções por HIV
/
Vacinas contra a AIDS
/
Modelos Imunológicos
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article