Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning

Résumé

Combining a set of phylogenetic trees into a single phylogenetic network that explains all of them is a fundamental challenge in evolutionary studies. In this paper, we apply the recently-introduced theoretical framework of cherry picking to design a class of heuristics that are guaranteed to produce a network containing each of the input trees, for practical-size datasets. The main contribution of this paper is the design and training of a machine learning model that captures essential information on the structure of the input trees and guides the algorithms towards better solutions. This is one of the first applications of machine learning to phylogenetic studies, and we show its promise with a proof-of-concept experimental study conducted on both simulated and real data consisting of binary trees with no missing taxa.
Fichier principal
Vignette du fichier
LIPIcs-WABI-2022-16.pdf (1.27 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03832882 , version 1 (28-10-2022)

Identifiants

Citer

Giulia Bernardini, Leo van Iersel, Esther Julien, Leen Stougie. Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning. WABI 2022 - 2nd International Workshop on Algorithms in Bioinformatics, Sep 2022, Potsdam, Germany. ⟨10.4230/LIPIcs.WABI.2022.16⟩. ⟨hal-03832882⟩
48 Consultations
82 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More