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    Investigations in phylogenetics: tree inference and model identifiability

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    Yourdkhani_S_2020.pdf
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    Author
    Yourdkhani, Samaneh
    Chair
    Rhodes, John A.
    Allman, Elizabeth S.
    Committee
    McIntyre, Julie
    Williams, Gordon
    Keyword
    phylogeny
    mathematical models
    evolutionary genetics
    biology
    computational biology
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/11303
    Abstract
    This thesis presents two projects in mathematical phylogenetics. The first presents a new, statistically consistent, fast method for inferring species trees from topological gene trees under the multispecies coalescent model. The algorithm of this method takes a collection of unrooted topological gene trees, computes a novel intertaxon distance from them, and outputs a metric species tree. The second establishes that numerical and non-numerical parameters of a specic Prole Mixture Model of protein sequence evolution are generically identifiable. Algebraic techniques are used, especially a theorem of Kruskal on tensor decomposition.
    Description
    Dissertation (Ph.D.) University of Alaska Fairbanks, 2020
    Table of Contents
    Chapter 1: Introduction. Chapter 2: Inferring Species Tree from Gene Trees -- 2.1 Introduction -- 2.2 Background and Notation -- 2.3 Weighted Rooted Triple Metrization of a Rooted Tree -- 2.4 Weighted Quartet Metrization of an Unrooted Tree -- 2.5 Weighted Quartet Distance Supertree and Consensus Algorithms -- 2.6 Algorithm Performance in Simulations -- References. Chapter 3: Identiability of a Protein Model -- 3.1 Abstract -- 3.2 Introduction -- 3.3 Markov Models on Trees -- 3.4 Algebraic Denitions and Lemmas -- 3.4.1 Denitions -- 3.4.2 Rank Propositions -- 3.5 Algebraic Aspects of the Prole Mixture Model -- 3.6 Identiability of Parameters for the Prole Mixture Model -- 3.7 Some other results -- References. Chapter 4: Conclusion and Future Work -- References -- Appendix.
    Date
    2020-05
    Type
    Dissertation
    Collections
    Mathematics and Statistics

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