Using gaussian process with test rejection to detect T-cell epitopes in pathogen genomes.
(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:
https://lup.lub.lu.se/record/818768
- author
- You, Liwen LU ; Brusic, V ; Gallagher, M and Bodén, M
- organization
- publishing date
- 2010
- 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
- pmid:21030740
- 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
- 2016-04-01 10:53:13
- date last changed
- 2024-01-07 03:44:22
@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}},
doi = {{10.1109/TCBB.2008.131}},
volume = {{7}},
year = {{2010}},
}