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Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.
Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris.
Afiliação
  • Choi I; Department of Computer Science, Dartmouth College, Hanover, New Hampshire, United States of America.
  • Chung AW; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Boston, Massachusetts, United States of America.
  • Suscovich TJ; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Boston, Massachusetts, United States of America.
  • Rerks-Ngarm S; Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand.
  • Pitisuttithum P; Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
  • Nitayaphan S; Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
  • Kaewkungwal J; Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
  • O'Connell RJ; Department of Retrovirology, U.S. Army Medical Component, AFRIMS, Bangkok, Thailand.
  • Francis D; Global Solutions for Infectious Diseases (GSID), South San Francisco, California, United States of America.
  • Robb ML; US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America; Henry Jackson Foundation HIV Program, US Military HIV Research Program, Bethesda, Maryland, United States of America.
  • Michael NL; US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America.
  • Kim JH; US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America.
  • Alter G; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Boston, Massachusetts, United States of America.
  • Ackerman ME; Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America.
  • Bailey-Kellogg C; Department of Computer Science, Dartmouth College, Hanover, New Hampshire, United States of America.
PLoS Comput Biol ; 11(4): e1004185, 2015 Apr.
Article em En | MEDLINE | ID: mdl-25874406
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional 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

Texto completo: 1 Coleções: 01-internacional 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