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dc.contributor.authorHeinson, Ashley
dc.contributor.authorGunawardana, Yawwani
dc.contributor.authorMoesker, Bastiaan
dc.contributor.authorHume, Carmen
dc.contributor.authorVataga, Elena
dc.contributor.authorHall, Yper
dc.contributor.authorStylianou, Elena
dc.contributor.authorMcShane, Helen
dc.contributor.authorWilliams, Ann
dc.contributor.authorNiranjan, Mahesan
dc.contributor.authorWoelk, Christopher
dc.date.accessioned2017-02-26T06:34:20Z
dc.date.available2017-02-26T06:34:20Z
dc.date.issued2017-02-01
dc.identifier.citationHeinson A, Gunawardana Y, Moesker B, Hume C, Vataga E, et al. (2017) Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology. International Journal of Molecular Sciences 18: 312. Available: http://dx.doi.org/10.3390/ijms18020312.
dc.identifier.issn1422-0067
dc.identifier.doi10.3390/ijms18020312
dc.identifier.urihttp://hdl.handle.net/10754/622919
dc.description.abstractReverse 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.
dc.description.sponsorshipThis work was performed with the support of the IRIDIS High Performance Computing Facility and the Bioinformatics Core at the University of Southampton and was funded by a Marie Curie Career Integration Grant (CIG, PCIG13-GA2013-618334).
dc.publisherMDPI AG
dc.relation.urlhttp://www.mdpi.com/1422-0067/18/2/312
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
dc.subjectBacterial pathogen
dc.subjectBacterial protective antigen
dc.subjectMachine learning
dc.subjectReverse vaccinology
dc.subjectSupport vector machine
dc.titleEnhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology
dc.typeArticle
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
dc.identifier.journalInternational Journal of Molecular Sciences
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionFaculty of Medicine, University of Southampton, Southampton, SO17 1BJ, United Kingdom
dc.contributor.institutionThermo Fisher Scientific, Inchinnan Business Park, 3 Fountain Drive, Paisley, PA4 9RF, United Kingdom
dc.contributor.institutionLondon School of Hygiene and Tropical Medicine (LSHTM), Department of Pathogen Molecular Biology, London, WC1E 7HT, United Kingdom
dc.contributor.institutioniSolutions, University of Southampton, Southampton, SO17 1BJ, United Kingdom
dc.contributor.institutionPublic Health England, National Infection Service, Porton Down, Salisbury, SP4 0JG, United Kingdom
dc.contributor.institutionThe Jenner Institute, University of Oxford, Oxford, OX3 7DQ, United Kingdom
dc.contributor.institutionDepartment of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom
kaust.personHume, Carmen
refterms.dateFOA2018-06-13T16:15:53Z


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