Journal article

Machine Learning Methods Enable Predictive Modeling of Antibody Feature: Function Relationships in RV144 Vaccinees

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

PLOS COMPUTATIONAL BIOLOGY | PUBLIC LIBRARY SCIENCE | Published : 2015

University of Melbourne Researchers

Grants

Awarded by Collaboration for AIDS Vaccine Discovery


Awarded by NSF


Awarded by National Health & Medical Research Center


Awarded by U.S. Army Medical Research and Material Command (USAMRMC)


Awarded by National Institutes of Allergy and Infectious Diseases


Awarded by Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.


Awarded by U.S. Department of Defense



Funding Acknowledgements

These studies were supported by the US Military HIV Research Program (MHRP), the Collaboration for AIDS Vaccine Discovery (OPP1032817: Leveraging Antibody Effector Function) to MEA, GA, and CBK, and NIH3R01Al080289-02S1 and 5R01Al080289-03 to GA. IC was supported by NSF grant IIS-0905206. AWC was supported by the American Australian Association (Amgen Fellowship) and National Health & Medical Research Center (NHMRC APP1036470). The work was also supported in part by an Interagency Agreement Y1-AI-2642-12 between the U.S. Army Medical Research and Material Command (USAMRMC) and the National Institutes of Allergy and Infectious Diseases and by a cooperative agreement (W81XWH-07-2-0067) between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. Department of Defense. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.