Search for dissertations about: "phylogenetic tree"
Showing result 1 - 5 of 103 swedish dissertations containing the words phylogenetic tree.
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1. Stochastic Models in Phylogenetic Comparative Methods: Analytical Properties and Parameter Estimation
Abstract : Phylogenetic comparative methods are well established tools for using inter-species variation to analyse phenotypic evolution and adaptation. They are generally hampered, however, by predominantly univariate approaches and failure to include uncertainty and measurement error in the phylogeny as well as the measured traits. READ MORE
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2. Probabilistic Models for Species Tree Inference and Orthology Analysis
Abstract : A phylogenetic tree is used to model gene evolution and species evolution using molecular sequence data. For artifactual and biological reasons, a gene tree may differ from a species tree, a phenomenon known as gene tree-species tree incongruence. Assuming the presence of one or more evolutionary events, e.g. READ MORE
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3. Climbing the Trichoptera Tree : Investigations of Branches and Leaves
Abstract : The Trichoptera (caddisflies) is the largest of the primary aquatic insect orders, currently including more than 13,500 species. With more than 100 species new to science described annually, the known caddisfly diversity is rapidly increasing. In the first four papers of this Thesis, a total of 22 species new to science are described. READ MORE
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4. Computational problems in evolution : Multiple alignment, genome rearrangements, and tree reconstruction
Abstract : Reconstructing the evolutionary history of a set of species is a fundamental problem in biology. This thesis concerns computational problems that arise in different settings and stages of phylogenetic tree reconstruction, but also in other contexts. READ MORE
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5. Development of New Methods for Inferring and Evaluating Phylogenetic Trees
Abstract : Inferring phylogeny is a difficult computational problem. Heuristics are necessary to minimize the time spent evaluating non optimal trees. In paper I, we developed an approach for heuristic searching, using a genetic algorithm. Genetic algorithms mimic the natural selections ability to solve complex problems. READ MORE