Methods for predicting vaccine immunogenicity and reactogenicity

• Main Authors: Patrícia Gonzalez-Dias (Author), Eva K. Lee (Author), Sara Sorgi (Author), Diógenes S. de Lima (Author), Alysson H. Urbanski (Author), Eduardo Lv Silveira (Author), Helder I. Nakaya (Author)
• Format: Book
• Published: Taylor & Francis Group, 2020-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2164-5515; 2164-554X; 10.1080/21645515.2019.1697110
• Keywords: systems vaccinology; machine learning; vaccine immunogenicity; vaccine reactogenicity; artificial intelligence; Immunologic diseases. Allergy; RC581-607; Therapeutics. Pharmacology; RM1-950
• Source: Human Vaccines & Immunotherapeutics, Vol 16, Iss 2, Pp 269-276 (2020)

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.