Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning

Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning

Bernardini, Giulia (University of Trieste; Centrum Wiskunde & Informatica (CWI))
van Iersel, L.J.J. (TU Delft Discrete Mathematics and Optimization)
Julien, E.A.T. (TU Delft Discrete Mathematics and Optimization)
Stougie, Leen (Centrum Wiskunde & Informatica (CWI); Vrije Universiteit Amsterdam; Erable)

Boucher, Christina (editor)
Rahmann, Sven (editor)

2022

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.

Cherry Picking
Heuristic
Hybridization
Machine Learning
Phylogenetics

http://resolver.tudelft.nl/uuid:29df3e7d-126e-4418-ae2e-3912af9ffc2b

https://doi.org/10.4230/LIPIcs.WABI.2022.16

Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing

9783959772433

22nd International Workshop on Algorithms in Bioinformatics, WABI 2022

22nd International Workshop on Algorithms in Bioinformatics, WABI 2022, 2022-09-05 → 2022-09-07, Potsdam, Germany

Leibniz International Proceedings in Informatics, LIPIcs, 1868-8969, 242

Institutional Repository

conference paper

© 2022 Giulia Bernardini, L.J.J. van Iersel, E.A.T. Julien, Leen Stougie