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Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning

Authors Giulia Bernardini , Leo van Iersel, Esther Julien, Leen Stougie

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Author Details

Giulia Bernardini
  • University of Trieste, Italy
  • CWI, Amsterdam, The Netherlands
Leo van Iersel
  • Delft Institute of Applied Mathematics, Delft University of Technology, The Netherlands
Esther Julien
  • Delft Institute of Applied Mathematics, Delft University of Technology, The Netherlands
Leen Stougie
  • CWI and Vrije Universiteit, Amsterdam, The Netherlands
  • Erable, France

Funding

This paper received funding from the Netherlands Organisation for Scientific Research (NWO) under project OCENW.GROOT.2019.015 "Optimization for and with Machine Learning (OPTIMAL)".
  • Bernardini, Giulia: MUR-FSE REACT EU - PON R&I 2014-2020
  • Stougie, Leen: The Netherlands Organisation for Scientific Research (NWO) under Gravitation-grant NETWORKS-024.002.003

Acknowledgements

The authors thank Remie Janssen for providing ideas and preliminary code for the randomised heuristics, and Yukihiro Murakami for the inspiring discussions.

Cite AsGet BibTex

Giulia Bernardini, Leo van Iersel, Esther Julien, and Leen Stougie. Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 16:1-16:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.WABI.2022.16

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
Keywords
  • Phylogenetics
  • Hybridization
  • Cherry Picking
  • Machine Learning
  • Heuristic

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Supplementary Materials

References

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