ArticleAnnual review of statistics and its application2023
Data integration in Bayesian phylogenetics.
Article in Annual review of statistics and its application, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A narrative review of spatial multi-omics and organ-on-a-chip technologies for cardio-cerebral-renal crosstalk.Frontiers in bioengineering and biotechnology · 2026Review
- Quantifying plasmid movement in drug-resistant Shigella species using phylodynamic inference.PLoS pathogens · 2025Article
- Quantum Speedups for Multiproposal MCMC.Bayesian analysis · 2025Article
- Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors.ArXiv · 2025Article
- Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors.Research square · 2025Article
- Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.Systematic biology · 2025Article
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.bioRxiv : the preprint server for biology · 2025Article
- Multi-response phylogenetic mixed models: concepts and application.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies.Bioinformatics and biology insights · 2025Article
- Fast Bayesian Inference of Phylogenies from Multiple Continuous Characters.Systematic biology · 2024Article
- Scalable Bayesian Divergence Time Estimation With Ratio Transformations.Systematic biology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.