Pediatric oncology, artificial intelligence, cancer diagnosis, machine learning, deep learning

Abstract

Immunoinformatics is an emerging area focused on development and applications of methods used to facilitate vaccine development. There is a growing interest in the field of vaccinology centered on the new omic science named ‘vaccinomics’. However, this approach has not succeeded to provide a solution against major infections affecting both animals and humans, since tick vaccines are still being developed based on conventional biochemical or immunological methods to dissect the molecular structure of the pathogen, looking for a candidate antigen. The availability of complete genomes and the novel advanced technologies, such as data mining, bioinformatics, microarrays, and proteomics, have revolutionized the approach to vaccine development and provided a new impulse to tick research. The aim of this review is to explore how modern vaccinology will contribute to the discovery of new candidate antigens and to understand the research process to improve existing vaccines. Under this concept, the omic age of ticks will make it possible to design vaccines starting from a prediction based on the in silico analysis of gene sequences obtained by data mining using computer algorithms, without the need to keep the pathogen growing in vitro. This new genome-based approach has been named “reverse vaccinology 3.0” or “vaccinomics 1.0” and can be applied to ticks.

Keywords

• rhipicephalus microplus
• tick vaccines
• reverse vaccinology
• vaccinomics
• systems biology