Using Gaussian process with test rejection to detect T-cell epitopes in pathogen genomes

Liwen You, Vladimir Brusic, Marcus Gallagher, Mikael Bodén

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

A major challenge in the development of peptide-based vaccines is finding the right immunogenic element, with efficient and long-lasting immunization effects, from large potential targets encoded by pathogen genomes. Computer models are convenient tools for scanning pathogen genomes to preselect candidate immunogenic peptides for experimental validation. Current methods predict many false positives resulting from a low prevalence of true positives. We develop a test reject method based on the prediction uncertainty estimates determined by Gaussian process regression. This method filters false positives among predicted epitopes from a pathogen genome. The performance of stand-alone Gaussian process regression is compared to other state-of-the-art methods using cross validation on 11 benchmark data sets. The results show that the Gaussian process method has the same accuracy as the top performing algorithms. The combination of Gaussian process regression with the proposed test reject method is used to detect true epitopes from the Vaccinia virus genome. The test rejection increases the prediction accuracy by reducing the number of false positives without sacrificing the method's sensitivity. We show that the Gaussian process in combination with test rejection is an effective method for prediction of T-cell epitopes in large and diverse pathogen genomes, where false positives are of concern.

Original languageEnglish
Article number4695825
Pages (from-to)741-751
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume7
Issue number4
DOIs
Publication statusPublished - 2010

Keywords

  • False positive
  • Gaussian processes
  • Immunology
  • Machine learning
  • Regression
  • amino acid sequence
  • epitope
  • false positives.
  • machine learning
  • regression

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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