ArticleJournal of mathematical biology2023
Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph.
Article in Journal of mathematical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Unifying phylogenetic traversal and deep learning to guide tree exploration.bioRxiv : the preprint server for biology · 2026Article
- Article
- larch: mapping the parsimony-optimal landscape of trees for directed exploration.bioRxiv : the preprint server for biology · 2025Article
- Finding high posterior density phylogenies by systematically extending a directed acyclic graph.Algorithms for molecular biology : AMB · 2025Article
- Leveraging DAGs to improve context-sensitive and abundance-aware tree estimation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities.PLoS computational biology · 2025Article
- Finding high posterior density phylogenies by systematically extending a directed acyclic graph.ArXiv · 2024Article
- The Structure of Deviations From Maximum Parsimony for Densely-Sampled Data and Applications for Clade Support Estimation.IEEE transactions on computational biology and bioinformaticsArticle
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5 authors.
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Abstract
In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty quantification.
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