[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123199-en":3,"doc-seo-123199-105":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},123199,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Predicting Phylogenetic Bootstrap Values via Machine Learning - Abstract","Estimating statistical robustness of inferred phylogenetic trees relies on branch support values for inner branches, with classic Felsenstein nonparametric bootstrap support (SBS) remaining the most widely used method under maximum likelihood. However, SBS is computationally expensive, motivating faster approximations such as rapid bootstrap, SH-aLRT, and UFBoot2, each with limitations. The presented educated bootstrap guesser (EBG), a machine-learning tool, predicts SBS values and provides per-branch uncertainty, achieving about 9.4x speed over UFBoot2 and low median absolute error across 0–100 support scores.","Predicting Phylogenetic Bootstrap Values via Machine Learning  \nJulius Wiegert ,*, 1 Dimitri Höhler ,1 Julia Haag ,1 Alexandros Stamatakis 1,2,3  \n1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany 2Biodiversity Computing Group, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece  \n3Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany  \n*Corresponding author: [E-mail: julius-wiegert@web.de](E-mail: julius-wiegert@web.de).  \nAssociate editor: Andrey Rzhetsky  \nAbstract  \nEstimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The still most widely used method for calculating branch support on trees inferred under maximum likelihood (ML) is the Standard, nonparametric Felsenstein bootstrap support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the rapid bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira– Hasegawa-like approximate likelihood ratio test) or the UltraFast bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or unstable behavior in the low support interval range (SH-aLRT). Here, we present the educated bootstrap guesser (EBG), a machine learningbased tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9 .4 (σ = 5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can, for instance, predict SBS support values on a phylogeny comprising 1,654 SARS-CoV2 genome sequences within 3 h on a mid-class laptop. EBG is available under GNU GPL3.  \nKey words: bootstrap support, machine learning, phylogenetics, uncertainty estimation.  \nIntroduction  \nInferring phylogenetic trees under the maximumlikelihood (ML) criterion is time—and resource-intensive, as the number of possible tree topologies increases superexponentially with the number of taxa under study. As a consequence, tree search algorithms, as implemented in RAxML-NG ( Kozlov et al. 2019), conduct their search fora best-known, yet not necessarily globally optimal, tree topology via a plethora of distinct heuristics. As there is no guarantee that the tree search will converge to the globally optimal tree, subsequent analyses to quantify its uncertainty are necessary. Such uncertainty analyses constitute an integral as well as routine component of current phylogenetic analysis pipelines (Kapli et al. 2020). The most common approach to quantify uncertainty is to infer various types of inner branch support values. The still most common technique to calculate branch support values on a resulting phylogeny under ML is the Standard, nonparametric classic Felsenstein bootstrap support (SBS)  \n( Felsenstein 1985). The SBS randomly samples the alignment sites of the multiple sequence alignment (MSA) with replacement to create a set of replicate MSAs (called bootstrap replicates). On each such replicate, one subsequently infers a respective ML tree. Thus, the SBS yields a set of bootstrap replicate ML trees. This SBS procedure is time-and resource-consuming, due to the high computational cost of conducting a phylogenetic tree inference on each replicate. Note that typically 100 to 500 replicate trees need to be inferred to obtain stable support values (Pattengale et al. 2010).  \nTo alleviate this computational bottleneck, a plethora of altern","cbCaii2V3GoKkbKA","https://ap.wps.com/l/cbCaii2V3GoKkbKA","pdf",1174229,1,13,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the educated bootstrap guesser (EBG) address in phylogenetic analyses?\",\"answer\":\"EBG addresses the high computational cost of classic Felsenstein bootstrap support (SBS) by predicting per-branch SBS values using machine learning while avoiding repeated expensive tree inference on many bootstrap replicates.\"},{\"question\":\"How does EBG compare with UFBoot2 in speed and accuracy?\",\"answer\":\"EBG is on average 9.4 times faster than UFBoot2 and achieves a median absolute error of 5 when predicting SBS values between 0 and 100.\"},{\"question\":\"What additional output does EBG provide beyond point predictions of SBS values?\",\"answer\":\"EBG provides uncertainty measures for all per-branch SBS predictions, enabling more rigorous and careful interpretation of branch support.\"}]","Predicting Phylogenetic Bootstrap Values via Machine Learning - Abstract | PDF",1785815173,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-phylogenetic-bootstrap-values-via-machine-learning-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-phylogenetic-bootstrap-values-via-machine-learning-abstract/123199/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the educated bootstrap guesser (EBG) address in phylogenetic analyses?","Question",{"text":75,"@type":76},"EBG addresses the high computational cost of classic Felsenstein bootstrap support (SBS) by predicting per-branch SBS values using machine learning while avoiding repeated expensive tree inference on many bootstrap replicates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does EBG compare with UFBoot2 in speed and accuracy?",{"text":80,"@type":76},"EBG is on average 9.4 times faster than UFBoot2 and achieves a median absolute error of 5 when predicting SBS values between 0 and 100.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional output does EBG provide beyond point predictions of SBS values?",{"text":84,"@type":76},"EBG provides uncertainty measures for all per-branch SBS predictions, enabling more rigorous and careful interpretation of branch support.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]