• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    ijms-18-00312-v2.pdf
    Size:
    1.162Mb
    Format:
    PDF
    Description:
    Main article
    Download
    Thumbnail
    Name:
    ijms-18-00312-s001.zip
    Size:
    2.430Mb
    Format:
    Unknown
    Description:
    Supplemental files
    Download
    Type
    Article
    Authors
    Heinson, Ashley
    Gunawardana, Yawwani
    Moesker, Bastiaan
    Hume, Carmen
    Vataga, Elena
    Hall, Yper
    Stylianou, Elena
    McShane, Helen cc
    Williams, Ann
    Niranjan, Mahesan
    Woelk, Christopher
    KAUST Department
    King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
    Date
    2017-02-01
    Permanent link to this record
    http://hdl.handle.net/10754/622919
    
    Metadata
    Show full item record
    Abstract
    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.
    Citation
    Heinson 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.
    Sponsors
    This 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).
    Publisher
    MDPI AG
    Journal
    International Journal of Molecular Sciences
    DOI
    10.3390/ijms18020312
    Additional Links
    http://www.mdpi.com/1422-0067/18/2/312
    ae974a485f413a2113503eed53cd6c53
    10.3390/ijms18020312
    Scopus Count
    Collections
    Articles

    entitlement

     
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.