Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees
Name
Choi-2015-Machine Learning Met.pdf
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Author(s)
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.
Date Issued
April 2015
Journal
PLOS Computational Biology
Publisher
Public Library of Science
Citation
Choi, Ickwon, Amy W. Chung, Todd J. Suscovich, Supachai Rerks-Ngarm, Punnee Pitisuttithum, Sorachai Nitayaphan, Jaranit Kaewkungwal, et al. “Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees.” Edited by Thomas B Kepler. PLoS Comput Biol 11, no. 4 (April 13, 2015): e1004185.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Ragon Institute of MGH, MIT and Harvard
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Creative Commons CC0
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1004185
