2026-07-23T01:41:44Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:17050 2024-03-06T10:58:37Z ddc:004 open_access

Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning Bernardini, Giulia van Iersel, Leo Julien, Esther Stougie, Leen Phylogenetics Hybridization Cherry Picking Machine Learning Heuristic 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Giulia Bernardini and Leo van Iersel and Esther Julien and Leen Stougie 2022 Is Part Of LIPIcs, Volume 242, 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.WABI.2022.16 urn:nbn:de:0030-drops-170507 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2022.16 eng https://creativecommons.org/licenses/by/4.0/legalcode