1. Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning
- Author
-
Bernardini, Giulia, van Iersel, Leo, Julien, Esther, Stougie, Leen, Boucher, Christina, Rahmann, Sven, Operations Analytics, Tinbergen Institute, Amsterdam Business Research Institute, Boucher, Christina, Rahmann, Sven, Università degli studi di Trieste = University of Trieste, Centrum Wiskunde & Informatica (CWI), Delft University of Technology (TU Delft), Vrije Universiteit Amsterdam [Amsterdam] (VU), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), 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)'. Giulia Bernardini: MUR-FSE REACT EU – PON R&I 2014-2020 Leen Stougie: The Netherlands Organisation for Scientific Research (NWO) under Gravitation-grant NETWORKS-024.002.003, Bernardini, Giulia, van Iersel, Leo, Julien, Esther, Stougie, Leen, and Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands
- Subjects
Machine Learning ,Phylogenetics ,Phylogenetic ,Cherry Picking, Machine Learning, Heuristic ,[SDV]Life Sciences [q-bio] ,Heuristic ,Hybridization ,Applied computing → Computational biology ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,Cherry Picking - 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., LIPIcs, Vol. 242, 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022), pages 16:1-16:22
- Published
- 2022