Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees
Suscovich, Todd J.
O'Connell, Robert J.
Robb, Merlin L.
Michael, Nelson L.
Kim, Jerome H.
Ackerman, Margaret E.
Bailey-Kellogg, ChrisNote: Order does not necessarily reflect citation order of authors.
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CitationChoi, I., A. W. Chung, T. J. Suscovich, S. Rerks-Ngarm, P. Pitisuttithum, S. Nitayaphan, J. Kaewkungwal, et al. 2015. “Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees.” PLoS Computational Biology 11 (4): e1004185. doi:10.1371/journal.pcbi.1004185. http://dx.doi.org/10.1371/journal.pcbi.1004185.
AbstractThe 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.
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