• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Algorithms for discovery of multiple Markov boundaries: application to the molecular signature multiplicity problem

    Statnikov, Alexander Romanovich
    : https://etd.library.vanderbilt.edu/etd-12042008-121803
    http://hdl.handle.net/1803/15093
    : 2008-12-06

    Abstract

    Algorithms for discovery of a Markov boundary from data constitute one of the most important recent developments in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight about local causal structure. Even though there is always a single Markov boundary of the response variable in faithful distributions, distributions with violations of the intersection property may have multiple Markov boundaries. Such distributions are abundant in practical data-analytic applications, and there are several reasons why it is important to induce all Markov boundaries from such data. However, there are currently no practical algorithms that can provably accomplish this task. To this end, I propose a novel generative algorithm (termed TIE*) that can discover all Markov boundaries from data. The generative algorithm can be instantiated to discover Markov boundaries independent of data distribution. I prove correctness of the generative algorithm and provide several admissible instantiations. The new algorithm is then applied to identify the set of maximally predictive and non-redundant molecular signatures. TIE* identifies exactly the set of true signatures in simulated distributions and yields signatures with significantly better predictivity and reproducibility than prior algorithms in human microarray gene expression datasets. The results of this thesis also shed light on the causes of molecular signature multiplicity phenomenon.
    Show full item record

    Files in this item

    Icon
    Name:
    Thesis.pdf
    Size:
    2.676Mb
    Format:
    PDF
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy