High throughput discovery and characterization of tick and pathogen vaccine protective antigens using vaccinomics with intelligent Big Data analytic techniques

• Main Authors: José De La Fuente (Author), Margarita Villar (Author), Agustin Estrada-Peña (Author), José A. Olivas (Author)
• Format: Book
• Published: Taylor & Francis Group, 2018-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1476-0584; 1744-8395; 10.1080/14760584.2018.1493928
• Keywords: tick; tick-borne diseases; vaccine; vaccinomics; big data analytics; graph theory; fuzzy deformable prototypes; Internal medicine; RC31-1245
• Source: Expert Review of Vaccines, Vol 17, Iss 7, Pp 569-576 (2018)

Introduction: The incidence of tick-borne diseases (TBDs) is growing worldwide, and vaccines appear as the most effective and environmentally sound intervention for the prevention and control of TBDs. Areas covered: The vaccinomics approach combines omics technologies and bioinformatics for the characterization of tick-host-pathogen molecular interactions and the development of next-generation vaccines. The two main challenges of the vaccinomics approach are the integration and analysis of omics datasets, and the development of screening platforms for the identification of candidate protective antigens. To address these challenges we propose the application of intelligent Big Data analytic techniques for the high throughput discovery and characterization of tick and pathogen derived candidate vaccine protective antigens. Expert commentary: This innovative approach should improve the development and efficacy of vaccines for the control and prevention of TBDs.