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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/35297
Title: Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees
Authors: Ickwon Choi
Amy W. Chung
Todd J. Suscovich
Supachai Rerks-Ngarm
Punnee Pitisuttithum
Sorachai Nitayaphan
Jaranit Kaewkungwal
Robert J. O'Connell
Donald Francis
Merlin L. Robb
Nelson L. Michael
Jerome H. Kim
Galit Alter
Margaret E. Ackerman
Chris Bailey-Kellogg
Dartmouth College
Massachusetts General Hospital
Thailand Ministry of Public Health
Mahidol University
Armed Forces Research Institute of Medical Sciences, Thailand
Global Solutions for Infectious Diseases
Walter Reed Army Institute of Research
Henry Jackson Foundation
Thayer School of Engineering at Dartmouth
Keywords: Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology;Computer Science;Environmental Science;Mathematics
Issue Date: 1-Jan-2015
Citation: PLoS Computational Biology. Vol.11, No.4 (2015)
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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84929485998&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/35297
ISSN: 15537358
1553734X
Appears in Collections:Scopus 2011-2015

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