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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.WABI.2022.16
URN: urn:nbn:de:0030-drops-170507
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/17050/
Bernardini, Giulia ;
van Iersel, Leo ;
Julien, Esther ;
Stougie, Leen
Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning
Abstract
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.
BibTeX - Entry
@InProceedings{bernardini_et_al:LIPIcs.WABI.2022.16,
author = {Bernardini, Giulia and van Iersel, Leo and Julien, Esther and Stougie, Leen},
title = {{Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning}},
booktitle = {22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
pages = {16:1--16:22},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-243-3},
ISSN = {1868-8969},
year = {2022},
volume = {242},
editor = {Boucher, Christina and Rahmann, Sven},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/17050},
URN = {urn:nbn:de:0030-drops-170507},
doi = {10.4230/LIPIcs.WABI.2022.16},
annote = {Keywords: Phylogenetics, Hybridization, Cherry Picking, Machine Learning, Heuristic}
}
