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Machine Learning for Predicting Vaccine Immunogenicity

Author

Listed:
  • 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)

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.

Suggested Citation

  • Eva K. Lee & Helder I. Nakaya & Fan Yuan & Troy D. Querec & Greg Burel & Ferdinand H. Pietz & Bernard A. Benecke & Bali Pulendran, 2016. "Machine Learning for Predicting Vaccine Immunogenicity," Interfaces, INFORMS, vol. 46(5), pages 368-390, October.
    • Handle: RePEc:inm:orinte:v:46:y:2016:i:5:p:368-390
    DOI: 10.1287/inte.2016.0862
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    References listed on IDEAS

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    1. H. Edwin Romeijn & Panos M. Pardalos (ed.), 2009. "Handbook of Optimization in Medicine," Springer Optimization and Its Applications, Springer, number 978-0-387-09770-1, December.
    2. Eva K. Lee & Tsung-Lin Wu, 2009. "Classification and Disease Prediction Via Mathematical Programming," Springer Optimization and Its Applications, in: H. Edwin Romeijn & Panos M. Pardalos (ed.), Handbook of Optimization in Medicine, chapter 12, pages 381-430, Springer.
    3. Marissa Fessenden, 2015. "The cell menagerie: human immune profiling," Nature, Nature, vol. 525(7569), pages 409-411, September.
    4. J. Brooks & Eva Lee, 2010. "Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model," Annals of Operations Research, Springer, vol. 174(1), pages 147-168, February.
    5. Eva K. Lee & Hany Y. Atallah & Michael D. Wright & Eleanor T. Post & Calvin Thomas & Daniel T. Wu & Leon L. Haley, 2015. "Transforming Hospital Emergency Department Workflow and Patient Care," Interfaces, INFORMS, vol. 45(1), pages 58-82, February.
    6. Rajesh Ravindran & Jens Loebbermann & Helder I. Nakaya & Nooruddin Khan & Hualing Ma & Leonardo Gama & Deepa K. Machiah & Benton Lawson & Paul Hakimpour & Yi-chong Wang & Shuzhao Li & Prachi Sharma & , 2016. "The amino acid sensor GCN2 controls gut inflammation by inhibiting inflammasome activation," Nature, Nature, vol. 531(7595), pages 523-527, March.
    7. Eva K. Lee & Richard J. Gallagher & David A. Patterson, 2003. "A Linear Programming Approach to Discriminant Analysis with a Reserved-Judgment Region," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 23-41, February.
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    Cited by:

    1. Bates, Thomas W. & Neyland, Jordan B. & Wang, Yolanda Yulong, 2018. "Financing acquisitions with earnouts," Journal of Accounting and Economics, Elsevier, vol. 66(2), pages 374-395.

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