[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126897-en":3,"doc-seo-126897-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126897,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning","The hybridization problem reconciles conflicting phylogenetic trees into a single phylogenetic network using the fewest reticulation nodes, yet it is computationally hard and prior methods mainly handle small or tightly restricted inputs. To address realistic multifurcating data with differing taxon sets, a heuristic framework called FHyNCH is introduced. It combines cherry-picking strategies with two purpose-designed machine-learning models. Experiments on synthetic and real datasets show the approach is practical and yields qualitatively good solutions.","Molecular Phylogenetics and Evolution 199 (2024) 108137  \nContents lists available at ScienceDirect  \nMolecular Phylogenetics and Evolution  \njournal [homepage:](homepage: www.elsevier.com/locate/ympev)[ www.elsevier.com/locate/ympev](homepage: www.elsevier.com/locate/ympev)  \n| Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning\u003Cbr>Giulia Bernardini a, * , Leo van Ierselb , Esther Julien b, * , Leen Stougie c, d, e\u003Cbr>a University of Trieste, Trieste, Italy\u003Cbr>b Delft Institute of Applied Mathematics, Delft, The Netherlands c CWI, Amsterdam, the Netherlands\u003Cbr>d Vrije Universiteit, Amsterdam, The Netherlands e INRIA-Erable, France |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Hybrid phylogeny Hybridization problem Cherry-picking Machine learning Heuristic |  | The Hybridization problem asks to reconcile a set of conflicting phylogenetic trees into a single phylogenetic network with the smallest possible number of reticulation nodes. This problem is computationally hard and previous solutions are limited to small and/or severely restricted data sets, for example, a set of binary trees with the same taxon set or only two non-binary trees with non-equal taxon sets. Building on our previous work on binary trees, we present FHyNCH, the first algorithmic framework to heuristically solve the Hybridization problem for large sets of multifurcating trees whose sets of taxa may differ. Our heuristics combine the cherrypicking technique, recently proposed to solve the same problem for binary trees, with two carefully designed machine-learning models. We demonstrate that our methods are practical and produce qualitatively good solutions through experiments on both synthetic and real data sets. |\n\n1. Introduction  \nUntil recently, the evolutionary history of a set of species was normally modeled as a rooted phylogenetic tree. However, the greater availability of molecular data is encouraging a paradigm shift to multilocus approaches for phylogenetic inference, which often leads to discovering relationships among the species that deviate from the simple model of a tree (Huson et al., 2010; Nakhleh, 2010; Bapteste et al., 2013). Indeed, the phylogenetic trees inferred from different loci of the genomes often have conflicting branching patterns, due to evolutionary events like recombination, hybrid speciation, introgression or lateral gene transfer (Randal Linder and Rieseberg, 2004; Mallet, 2005; Boto, 2010; Mallet et al., 2016). In the presence of such events, evolution is more accurately represented by a rooted phylogenetic network, which extends the tree model and allows representing multi-parental inheritance of genetic material as reticulation nodes (Randal Linder et al., 2004; Mallet et al., 2016).  \nA crucial problem is then to infer a single phylogenetic network from a set of conflicting trees built from different loci of the genomes in a data set. A commonly used criterion to estimate such a network, which is  \nreasonable when discordance between trees is believed to be caused by multi-parent inheritance, is parsimony (Huson et al., 2010): the goal is then to construct a network that simultaneously explains all ancestral relationships encoded by the trees with the fewest number of reticulation nodes. This problem is known in the literature by the name of HYBRIDIZATION and has been extensively studied.  \nHYBRIDIZATION has been shown to be NP-hard even for two binary input trees (Bordewich and Semple, 2007). Most of the solutions proposed in the literature are limited to inputs consisting of only two binary trees with identical leaf sets. A few methods exist that waive some of these assumptions: some admit inputs consisting of several binary trees with identical (van Iersel et al., 2022) or largely overlapping (Bernardiniet al., 2022; Bernardini et al., 2023) leaf sets; others are able to process a pair of multifurcating (i.e. nonbinary) trees wi","cbCaiakKLle5U0aJ","https://ap.wps.com/l/cbCaiakKLle5U0aJ","pdf",1123257,1,12,"English","en",105,"# Introduction\n## Modeling evolutionary history: trees vs networks\n## The HYBRIDIZATION problem and parsimony\n## Prior work limitations\n# Proposed framework: FHyNCH","[{\"question\":\"What is the hybridization problem in phylogenetics?\",\"answer\":\"It asks to reconcile a set of conflicting phylogenetic trees into one phylogenetic network while minimizing the number of reticulation nodes.\"},{\"question\":\"Why are existing hybridization methods limited?\",\"answer\":\"Most approaches assume only two binary input trees with identical leaf sets, or they relax assumptions only to small and restricted cases such as overlapping or largely overlapping taxa.\"},{\"question\":\"How does FHyNCH infer hybridization networks for multifurcating trees?\",\"answer\":\"It uses cherry-picking heuristics combined with two tailored machine-learning models to guide the search for feasible, qualitatively good networks.\"}]","Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning | 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is the hybridization problem in phylogenetics?","Question",{"text":75,"@type":76},"It asks to reconcile a set of conflicting phylogenetic trees into one phylogenetic network while minimizing the number of reticulation nodes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are existing hybridization methods limited?",{"text":80,"@type":76},"Most approaches assume only two binary input trees with identical leaf sets, or they relax assumptions only to small and restricted cases such as overlapping or largely overlapping taxa.",{"name":82,"@type":73,"acceptedAnswer":83},"How does FHyNCH infer hybridization networks for multifurcating trees?",{"text":84,"@type":76},"It uses cherry-picking heuristics combined with two tailored machine-learning models to guide the search for feasible, qualitatively good 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