{"doi":"10.1109/tcbb.2024.3457875","title":"An Automated Convergence Diagnostic for Phylogenetic MCMC Analyses","abstract":null,"journal":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","year":2024,"id":649201,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1401012,"name":"Remco Bouckaert","orcid":"0000-0001-5186-8377","position":1,"is_corresponding":false},{"id":955842,"name":"Alex Gavryushkin","orcid":"0000-0001-6299-8249","position":2,"is_corresponding":false},{"id":1692294,"name":"Lars Berling","orcid":"0000-0002-1303-0669","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"An Automated Convergence Diagnostic for Phylogenetic MCMC Analyses","abstract":"Assessing convergence of Markov chain Monte Carlo (MCMC) based analyses is crucial but challenging, especially so in high dimensional and complex spaces such as the space of phylogenetic trees (treespace). In practice, it is assumed that the target distribution is the unique stationary distribution of the MCMC and convergence is achieved when samples appear to be stationary. Here we leverage recent advances in computational geometry of the treespace and introduce a method that combines classical statistical techniques and algorithms with geometric properties of the treespace to automatically evaluate and assess practical convergence of phylogenetic MCMC analyses. Our method monitors convergence across multiple MCMC chains and achieves high accuracy in detecting both practical convergence and convergence issues within treespace. Furthermore, our approach is developed to allow for real-time evaluation during the MCMC algorithm run, eliminating any of the chain post-processing steps that are currently required. Our tool therefore improves reliability and efficiency of MCMC based phylogenetic inference methods and makes analyses easier to reproduce and compare. We demonstrate the efficacy of our diagnostic via a well-calibrated simulation study and provide examples of its performance on real data sets. Although our method performs well in practice, a significant part of the underlying treespace probability theory is still missing, which creates an excellent opportunity for future mathematical research in this area.","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39255085","pmcid":null,"openalex_id":"https://openalex.org/W4402401545","authors":[],"funders":[{"funder_name":"Royal Society Te Apārangi","grant_id":"UOC1702","title":null},{"funder_name":"Marsden","grant_id":"21-UOC-057","title":null},{"funder_name":"Ministry of Business, Innovation, and Employment of New Zealand","grant_id":"UOOX1912","title":null},{"funder_name":"Data Science Programmes","grant_id":"UOAX1932","title":null}],"total_grants":4,"fwci":2.0603,"citation_percentile":0.87962644,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx8/8857/10790554/10675342.pdf?arnumber=10675342","host_type":"publisher"},{"url":"https://doi.org/10.1109/tcbb.2024.3457875","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39255085","host_type":"repository"}],"fields_of_study":["Evolution and Paleontology Studies","Genomics and Phylogenetic Studies","Bayesian Methods and Mixture Models","Phylogeny","Markov Chains","Algorithms","Monte Carlo Method","Computational Biology","Computer Simulation"],"mesh_terms":["Algorithms","Computer Simulation","Markov Chains","Models, Genetic","Monte Carlo Method","Phylogeny","Computational Biology"],"keywords":["Markov chain Monte Carlo","Phylogenetic tree","Convergence (economics)","Computer science","Computational biology","Artificial intelligence","Biology","Bayesian probability","Genetics","Economics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Climate action"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T03:22:37.888978Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}