Methods for predicting vaccine immunogenicity and reactogenicity

Author(s)

Gonzalez-Dias, Patricia ^[1] ; Lee, Eva K. ^[2] ; Sorgi, Sara ^[3] ; de Lima, Diogenes S. ^[1] ; Urbanski, Alysson H. ^[1] ; Silveira, Eduardo Lv ^[1] ; Nakaya, Helder I. ^{[1, 4]}

Affiliation

^[1] Univ Sao Paulo, Sch Pharmaceut Sci, Dept Clin & Toxicol Anal, BR-05508000 Sao Paulo - Brazil

^[2] Georgia Inst Technol, Ctr Operat Res Med & HealthCare, Atlanta, GA 30332 - USA

^[3] Univ Siena, Dept Med Biotechnol, Siena - Italy

^[4] Univ Sao Paulo, Sci Platform Pasteur, Sao Paulo - Brazil

• Document type: Review article
• Source: HUMAN VACCINES & IMMUNOTHERAPEUTICS; v. 16, n. 2 DEC 2019.
• Web of Science Citations: 1

Abstract

Subjects receiving the same vaccine often show different levels of immune responses and some may even present adverse side effects to the vaccine. Systems vaccinology can combine omics data and machine learning techniques to obtain highly predictive signatures of vaccine immunogenicity and reactogenicity. Currently, several machine learning methods are already available to researchers with no background in bioinformatics. Here we described the four main steps to discover markers of vaccine immunogenicity and reactogenicity: (1) Preparing the data; (2) Selecting the vaccinees and relevant genes; (3) Choosing the algorithm; (4) Blind testing your model. With the increasing number of Systems Vaccinology datasets being generated, we expect that the accuracy and robustness of signatures of vaccine reactogenicity and immunogenicity will significantly improve. (AU)

• FAPESP's process: 13/08216-2 - CRID - Center for Research in Inflammatory Diseases
• Grantee: Fernando de Queiroz Cunha
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
• Grantee: André Fujita
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/14933-2 - Integrative biology applied to human health
• Grantee: Helder Takashi Imoto Nakaya
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2