Shared more. Cited more. Safe forever.
    • advanced search
    • submit works
    • about
    • help
    • contact us
    • login
    View Item 
    •   MOspace Home
    • University of Missouri-Columbia
    • Office of Undergraduate Research (MU)
    • Undergraduate Research and Creative Achievements Forum (MU)
    • 2008 Undergraduate Research and Creative Achievements Forum (MU)
    • View Item
    •   MOspace Home
    • University of Missouri-Columbia
    • Office of Undergraduate Research (MU)
    • Undergraduate Research and Creative Achievements Forum (MU)
    • 2008 Undergraduate Research and Creative Achievements Forum (MU)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    advanced searchsubmit worksabouthelpcontact us

    Browse

    All of MOspaceCommunities & CollectionsDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis SemesterThis CollectionDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis Semester

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular AuthorsStatistics by Referrer

    Prediction of superhelical proteins using machine learning methods [abstract]

    Koenig, Brett
    Korkin, Dmitry
    View/Open
    [PDF] PredictionSuperhelicalProteins.pdf (26.17Kb)
    Date
    2008
    Contributor
    University of Missouri-Columbia. Office of Undergraduate Research
    Format
    Presentation
    Metadata
    [+] Show full item record
    Abstract
    The main objective is to use three state of the art machine learning methods to find the most efficient way for predicting and characterizing the superhelical proteins based solely on their sequence information. To achieve this goal we will first apply each method individually and then integrate all three methods to obtain the most efficient and accurate prediction. We first apply the support vector machine (SVM), a feature-based approach that requires training on the set of positive and negative examples. As the second method, we will use the Bayesian inference approach. Finally, we will employ the Hidden Markov Model (HMM), another popular machine learning technique widely used in bioinformatics. Expectations for the research are that combining all three learning methods at different prediction stages will result in superior performance and accuracy when compared to each individual approach.
    URI
    http://hdl.handle.net/10355/1936
    Part of
    2008 Undergraduate Research and Creative Achievements Forum (MU)
    Collections
    • 2008 Undergraduate Research and Creative Achievements Forum (MU)

    Send Feedback
    hosted by University of Missouri Library Systems
     

     


    Send Feedback
    hosted by University of Missouri Library Systems