Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning.
Mol Phylogenet Evol
; 199: 108137, 2024 Oct.
Article
em En
| MEDLINE
| ID: mdl-39029549
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Filogenia
/
Algoritmos
/
Aprendizado de Máquina
Idioma:
En
Revista:
Mol Phylogenet Evol
/
Mol. phylogenet. evol
/
Molecular phylogenetics and evolution
Assunto da revista:
BIOLOGIA
/
BIOLOGIA MOLECULAR
Ano de publicação:
2024
Tipo de documento:
Article