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

You, Liwen, Brusic, Vladimir, Gallagher, Marcus and Boden, Mikael (2010) Using Gaussian process with test rejection to detect T-Cell epitopes in pathogen genomes. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 7 4: 741-751. doi:10.1109/TCBB.2008.131

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Author You, Liwen
Brusic, Vladimir
Gallagher, Marcus
Boden, Mikael
Title Using Gaussian process with test rejection to detect T-Cell epitopes in pathogen genomes
Journal name IEEE-ACM Transactions on Computational Biology and Bioinformatics   Check publisher's open access policy
ISSN 1545-5963
1557-9964
Publication date 2010-10
Year available 2008
Sub-type Article (original research)
DOI 10.1109/TCBB.2008.131
Volume 7
Issue 4
Start page 741
End page 751
Total pages 11
Place of publication New York, United States
Publisher IEEE
Collection year 2011
Language eng
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.
Keyword Immunology
Amino acid sequence
Epitope
Machine learning
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online 1 Dec. 2008.

 
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Created: Sun, 14 Nov 2010, 00:02:06 EST