1,709 research outputs found
Learning Latent Tree Graphical Models
We study the problem of learning a latent tree graphical model where samples
are available only from a subset of variables. We propose two consistent and
computationally efficient algorithms for learning minimal latent trees, that
is, trees without any redundant hidden nodes. Unlike many existing methods, the
observed nodes (or variables) are not constrained to be leaf nodes. Our first
algorithm, recursive grouping, builds the latent tree recursively by
identifying sibling groups using so-called information distances. One of the
main contributions of this work is our second algorithm, which we refer to as
CLGrouping. CLGrouping starts with a pre-processing procedure in which a tree
over the observed variables is constructed. This global step groups the
observed nodes that are likely to be close to each other in the true latent
tree, thereby guiding subsequent recursive grouping (or equivalent procedures)
on much smaller subsets of variables. This results in more accurate and
efficient learning of latent trees. We also present regularized versions of our
algorithms that learn latent tree approximations of arbitrary distributions. We
compare the proposed algorithms to other methods by performing extensive
numerical experiments on various latent tree graphical models such as hidden
Markov models and star graphs. In addition, we demonstrate the applicability of
our methods on real-world datasets by modeling the dependency structure of
monthly stock returns in the S&P index and of the words in the 20 newsgroups
dataset
Combinatorics of least squares trees
A recurring theme in the least squares approach to phylogenetics has been the
discovery of elegant combinatorial formulas for the least squares estimates of
edge lengths. These formulas have proved useful for the development of
efficient algorithms, and have also been important for understanding
connections among popular phylogeny algorithms. For example, the selection
criterion of the neighbor-joining algorithm is now understood in terms of the
combinatorial formulas of Pauplin for estimating tree length.
We highlight a phylogenetically desirable property that weighted least
squares methods should satisfy, and provide a complete characterization of
methods that satisfy the property. The necessary and sufficient condition is a
multiplicative four point condition that the the variance matrix needs to
satisfy. The proof is based on the observation that the Lagrange multipliers in
the proof of the Gauss--Markov theorem are tree-additive. Our results
generalize and complete previous work on ordinary least squares, balanced
minimum evolution and the taxon weighted variance model. They also provide a
time optimal algorithm for computation
Reconciling taxonomy and phylogenetic inference: formalism and algorithms for describing discord and inferring taxonomic roots
Although taxonomy is often used informally to evaluate the results of
phylogenetic inference and find the root of phylogenetic trees, algorithmic
methods to do so are lacking. In this paper we formalize these procedures and
develop algorithms to solve the relevant problems. In particular, we introduce
a new algorithm that solves a "subcoloring" problem for expressing the
difference between the taxonomy and phylogeny at a given rank. This algorithm
improves upon the current best algorithm in terms of asymptotic complexity for
the parameter regime of interest; we also describe a branch-and-bound algorithm
that saves orders of magnitude in computation on real data sets. We also
develop a formalism and an algorithm for rooting phylogenetic trees according
to a taxonomy. All of these algorithms are implemented in freely-available
software.Comment: Version submitted to Algorithms for Molecular Biology. A number of
fixes from previous versio
Whole Genome Phylogenetic Tree Reconstruction Using Colored de Bruijn Graphs
We present kleuren, a novel assembly-free method to reconstruct phylogenetic
trees using the Colored de Bruijn Graph. kleuren works by constructing the
Colored de Bruijn Graph and then traversing it, finding bubble structures in
the graph that provide phylogenetic signal. The bubbles are then aligned and
concatenated to form a supermatrix, from which a phylogenetic tree is inferred.
We introduce the algorithms that kleuren uses to accomplish this task, and show
its performance on reconstructing the phylogenetic tree of 12 Drosophila
species. kleuren reconstructed the established phylogenetic tree accurately,
and is a viable tool for phylogenetic tree reconstruction using whole genome
sequences. Software package available at: https://github.com/Colelyman/kleurenComment: 6 pages, 3 figures, accepted at BIBE 2017. Minor modifications to the
text due to reviewer feedback and fixed typo
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