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

You, Liwen LU ; Brusic, V; Gallagher, M and Bodén, M (2010) In IEEE/ACM Transactions on Computational Biology & Bioinformatics 7(4). p.741-751
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE/ACM Transactions on Computational Biology & Bioinformatics
volume
7
issue
4
pages
741 - 751
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:21030740
  • wos:000283559100017
  • scopus:78149281499
ISSN
1557-9964
DOI
10.1109/TCBB.2008.131
language
English
LU publication?
yes
id
44bd0987-b7d2-44ef-9458-074fdc324b7c (old id 818768)
date added to LUP
2008-01-03 09:13:18
date last changed
2018-05-29 11:00:44
@article{44bd0987-b7d2-44ef-9458-074fdc324b7c,
  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.},
  author       = {You, Liwen and Brusic, V and Gallagher, M and Bodén, M},
  issn         = {1557-9964},
  language     = {eng},
  number       = {4},
  pages        = {741--751},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE/ACM Transactions on Computational Biology & Bioinformatics},
  title        = {Using gaussian process with test rejection to detect T-cell epitopes in pathogen genomes.},
  url          = {http://dx.doi.org/10.1109/TCBB.2008.131},
  volume       = {7},
  year         = {2010},
}