A novel strategy for classifying the output from an in silicovaccine discovery pipeline for eukaryotic pathogens using machine learning algorithmsReport as inadecuate




A novel strategy for classifying the output from an in silicovaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms - Download this document for free, or read online. Document in PDF available to download.

BMC Bioinformatics

, 14:315

First Online: 02 November 2013Received: 20 June 2013Accepted: 28 October 2013

Abstract

BackgroundAn 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.

ResultsThe 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.

ConclusionsVaccine 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-14-315 contains supplementary material, which is available to authorized users.

Download fulltext PDF



Author: Stephen J Goodswen - Paul J Kennedy - John T Ellis

Source: https://link.springer.com/







Related documents