Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning
Giulia Bernardini (University of Trieste, Centrum Wiskunde & Informatica (CWI))
Leo Van Iersel (TU Delft - Discrete Mathematics and Optimization)
E.A.T. Julien (TU Delft - Discrete Mathematics and Optimization)
Leen Stougie (Vrije Universiteit Amsterdam, Centrum Wiskunde & Informatica (CWI), Erable)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.
help_outline