Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning.

Autor: Bernardini G; University of Trieste, Trieste, Italy. Electronic address: giulia.bernardini@units.it., van Iersel L; Delft Institute of Applied Mathematics, Delft, The Netherlands., Julien E; Delft Institute of Applied Mathematics, Delft, The Netherlands. Electronic address: E.A.T.Julien@tudelft.nl., Stougie L; CWI, Amsterdam, the Netherlands; Vrije Universiteit, Amsterdam, The Netherlands; INRIA-Erable, France.
Jazyk: angličtina
Zdroj: Molecular phylogenetics and evolution [Mol Phylogenet Evol] 2024 Oct; Vol. 199, pp. 108137. Date of Electronic Publication: 2024 Jul 17.
DOI: 10.1016/j.ympev.2024.108137
Abstrakt: The Hybridization problem asks to reconcile a set of conflicting phylogenetic trees into a single phylogenetic network with the smallest possible number of reticulation nodes. This problem is computationally hard and previous solutions are limited to small and/or severely restricted data sets, for example, a set of binary trees with the same taxon set or only two non-binary trees with non-equal taxon sets. Building on our previous work on binary trees, we present FHyNCH, the first algorithmic framework to heuristically solve the Hybridization problem for large sets of multifurcating trees whose sets of taxa may differ. Our heuristics combine the cherry-picking technique, recently proposed to solve the same problem for binary trees, with two carefully designed machine-learning models. We demonstrate that our methods are practical and produce qualitatively good solutions through experiments on both synthetic and real data sets.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: