title: A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.
abstract: BACKGROUND
  An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.
  
  
  RESULTS
  The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally.
  
  
  CONCLUSIONS
  Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Goodswen
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Kennedy
authors.affiliation: School of Medical and Molecular Biosciences, ithree institute at the University of Technology Sydney (UTS), Sydney, Australia. John.Ellis@uts.edu.au.
authors.forename: John T
authors.initials: JT
authors.lastname: Ellis
mesh_terms: D000465:Algorithms; D000818:Animals; D000941:Antigens / Q000737:chemistry / Q000276:immunology*; D001185:Artificial Intelligence; D029742:Caenorhabditis elegans Proteins / Q000737:chemistry / Q000276:immunology*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery; D015800:Protozoan Proteins / Q000737:chemistry / Q000276:immunology*; D012680:Sensitivity and Specificity; D014612:Vaccines / Q000737:chemistry / Q000276:immunology*
chemical_list: D000941:Antigens; D029742:Caenorhabditis elegans Proteins; D015800:Protozoan Proteins; D014612:Vaccines