Machine Learning for Predicting Vaccine Immunogenicity

Eva K. Lee (), Helder I. Nakaya (), Fan Yuan, Troy D. Querec, Greg Burel, Ferdinand H. Pietz, Bernard A. Benecke and Bali Pulendran
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Eva K. Lee: NSF-Whitaker Center for Operations Research in Medicine and HealthCare, Atlanta, Georgia 30332; and NSF I/UCRC Center for Health Organization Transformation, Atlanta, Georgia 30332; and Industrial and Systems Engineering and Computer Science, Georgia Institute of Technology, Atlanta, Georgia 30332
Helder I. Nakaya: School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil; and Department of Pathology, School of Medicine, Emory University, Atlanta, Georgia 30329
Fan Yuan: NSF-Whitaker Center for Operations Research in Medicine and HealthCare, Atlanta, Georgia 30332; and NSF I/UCRC Center for Health Organization Transformation, Atlanta, Georgia 30332; and Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Troy D. Querec: Chronic Viral Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia 30322
Greg Burel: Strategic National Stockpile, Centers for Disease Control and Prevention, Atlanta, Georgia 30322
Ferdinand H. Pietz: Strategic National Stockpile, Centers for Disease Control and Prevention, Atlanta, Georgia 30322
Bernard A. Benecke: Global Disease Detection and Emergency Response, Centers for Disease Control and Prevention, Atlanta, Georgia 30322
Bali Pulendran: Department of Microbiology and Immunology, Emory University School of Medicine; and Department of Pathology, School of Medicine, Emory University, Atlanta, Georgia 30329

Interfaces, 2016, vol. 46, issue 5, 368-390

Abstract: The ability to predict how different individuals will respond to vaccination and to understand what best protects individuals from infection greatly facilitates developing next-generation vaccines. It facilitates both the rapid design and evaluation of new and emerging vaccines and identifies individuals unlikely to be protected by vaccine. We describe a general-purpose machine-learning framework, DAMIP, for discovering gene signatures that can predict vaccine immunity and efficacy. DAMIP is a multiple-group, concurrent classifier that offers unique features not present in other models: a nonlinear data transformation to manage the curse of dimensionality and noise; a reserved-judgment region that handles fuzzy entities; and constraints on the allowed percentage of misclassifications.Using DAMIP, implemented results for yellow fever demonstrated that, for the first time, a vaccine’s ability to immunize a patient could be successfully predicted (with accuracy of greater than 90 percent) within one week after vaccination. A gene identified by DAMIP, EIF2AK4, decrypted a seven-decade-old mystery of vaccination. Results for flu vaccine demonstrated DAMIP’s applicability to both live-attenuated and inactivated vaccines. Results in a malaria study enabled targeted delivery to individual patients.Our project’s methods and findings permit highlighting and probabilistically prioritizing hypothesis design to enhance biological discovery. Moreover, they guide the rapid development of better vaccines to fight emerging infections, and improve monitoring for poor responses in the elderly, infants, or others with weakened immune systems. In addition, the project’s work should help with universal flu-vaccine design.

Keywords: machine learning; multiple-group classification; vaccine immunogenicity prediction; influenza; yellow fever; malaria; health security; prophylactic medical countermeasures; hypothesis generation; vaccine design for emerging infections (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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