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Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology
Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO(®) vaccine against Neisseria meningit...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343848/ https://www.ncbi.nlm.nih.gov/pubmed/28157153 http://dx.doi.org/10.3390/ijms18020312 |
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author | Heinson, Ashley I. Gunawardana, Yawwani Moesker, Bastiaan Denman Hume, Carmen C. Vataga, Elena Hall, Yper Stylianou, Elena McShane, Helen Williams, Ann Niranjan, Mahesan Woelk, Christopher H. |
author_facet | Heinson, Ashley I. Gunawardana, Yawwani Moesker, Bastiaan Denman Hume, Carmen C. Vataga, Elena Hall, Yper Stylianou, Elena McShane, Helen Williams, Ann Niranjan, Mahesan Woelk, Christopher H. |
author_sort | Heinson, Ashley I. |
collection | PubMed |
description | Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO(®) vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future. |
format | Online Article Text |
id | pubmed-5343848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53438482017-03-16 Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology Heinson, Ashley I. Gunawardana, Yawwani Moesker, Bastiaan Denman Hume, Carmen C. Vataga, Elena Hall, Yper Stylianou, Elena McShane, Helen Williams, Ann Niranjan, Mahesan Woelk, Christopher H. Int J Mol Sci Article Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO(®) vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future. MDPI 2017-02-01 /pmc/articles/PMC5343848/ /pubmed/28157153 http://dx.doi.org/10.3390/ijms18020312 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Heinson, Ashley I. Gunawardana, Yawwani Moesker, Bastiaan Denman Hume, Carmen C. Vataga, Elena Hall, Yper Stylianou, Elena McShane, Helen Williams, Ann Niranjan, Mahesan Woelk, Christopher H. Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology |
title | Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology |
title_full | Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology |
title_fullStr | Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology |
title_full_unstemmed | Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology |
title_short | Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology |
title_sort | enhancing the biological relevance of machine learning classifiers for reverse vaccinology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343848/ https://www.ncbi.nlm.nih.gov/pubmed/28157153 http://dx.doi.org/10.3390/ijms18020312 |
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