Lee, Eva K.
de Lima, Diogenes S.
Urbanski, Alysson H.
Silveira, Eduardo Lv
Nakaya, Helder I.
Número total de Autores: 7
Afiliação do(s) autor(es):
 Univ Sao Paulo, Sch Pharmaceut Sci, Dept Clin & Toxicol Anal, BR-05508000 Sao Paulo - Brazil
 Georgia Inst Technol, Ctr Operat Res Med & HealthCare, Atlanta, GA 30332 - USA
 Univ Siena, Dept Med Biotechnol, Siena - Italy
 Univ Sao Paulo, Sci Platform Pasteur, Sao Paulo - Brazil
Número total de Afiliações: 4
Tipo de documento:
Artigo de Revisão
HUMAN VACCINES & IMMUNOTHERAPEUTICS;
Citações Web of Science:
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)