Authors

SJ Goodswen¹; PJ Kennedy²; JT Ellis¹;  ¹ School of Life Sciences, University of Technology Sydney, Australia;  ² School of Computer Science, Faculty of Engineering and Information Technology and the Australian Artificial Intelligence Institute, University of Technology Sydney, Australia

Discussion

Vaccination is probably considered the most efficient tool for preventing current and future threats from parasitic diseases. Immunogenic proteins sourced from parasites are often considered worthwhile vaccine components (subunits) and studied further for their vaccine potential. Very few parasite proteins have achieved proof of concept trials in animal models as vaccines and so be considered as being true vaccine candidates for future development. Publications on parasites with ‘subunit vaccine’ in their title have accumulated to thousands over the last three decades. However, there are possibly thousands more reporting immunogenicity results without mentioning ‘subunit’ and/or ‘vaccine’. The exact number is unclear given the non-standardised keywords in publications. The aim of this study was to identify parasite proteins that induce a protective response in an animal model as reported in the scientific literature within the last 30 years using machine learning and natural language processing.Ultimately, we believe this data set has future value in the development of reverse vaccinology approaches for parasites.