Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

Stephen J Goodswen1, Paul J Kennedy2 and John T Ellis1*

Author Affiliations

1 School of Medical and Molecular Biosciences, ithree institute at the University of Technology Sydney (UTS), Sydney, Australia

2 School of Software, Faculty of Engineering and Information Technology and the Centre for Quantum Computation and Intelligent Systems at the University of Technology Sydney (UTS), Sydney, Australia

For all author emails, please log on.

BMC Bioinformatics 2013, 14:315  doi:10.1186/1471-2105-14-315

Published: 2 November 2013

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.