Author
A. J. Drummond
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Biology Disciplines
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2017-2024 · peak 2/yrCo-Author Collaboration Network
10 top collaborators · 30 mutual co-author links6 papers
Recent·Most cited
LinguaPhylo: a probabilistic model specification language for reproducible phylogenetic analyses
A. J. Drummond, K. Chen, F. K. Mendes, D. Xie
This paper introduces a new model specification language, LPhy, for phylogenetic analyses to improve reproducibility and clarity in model descriptions.
PLOS Computational Biology · Jul 18, 2023Peer-reviewedEvolutionary Biology
Novel Integrative Modeling of Molecules and Morphology across Evolutionary Timescales
H. A. Ogilvie, F. K. Mendes, T. G. Vaughan, N. J. Matzke, T. Stadler, D. Welch, A. J. Drummond
This paper introduces a new evolutionary model, FBD-MSC, that integrates genealogical and fossilization processes to improve divergence time estimates in Caninae.
bioRxiv · Jun 16, 2021Evolutionary Biology
Accounting for errors in data improves timing in single-cell cancer evolution
K. Chen, J. C. Moravec, A. Gavryushkin, D. Welch, A. J. Drummond
This study improves timing accuracy in single-cell cancer evolution by modeling errors in single-cell DNA sequencing data using BEAST2, showing that error accounting shifts divergence times forward and reduces overestimation of phylogenetic diversity.
bioRxiv · Mar 17, 2021 · PreprintEvolutionary Biology
BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis
R. Bouckaert, T. G. Vaughan, J. Barido-Sottani, S. Duchene, M. Fourment, A. Gavryushkina, J. Heled, G. Jones, D. Kuhnert, N. de Maio, M. Matschiner, F. K. Mendes, N. Muller, H. A. Ogilvie, L. du Plessis, , A. J. Drummond
The paper describes new developments in the BEAST 2.5 software platform for Bayesian evolutionary analysis, expanding joint inference over multiple data types and complex phylodynamics.
bioRxiv · Nov 19, 2018Evolutionary Biology
Bayesian Inference of Species Networks from Multilocus Sequence Data
C. Zhang, H. A. Ogilvie, A. J. Drummond, T. Stadler
This paper introduces a Bayesian method to infer species networks and gene trees from multilocus sequence data, demonstrating its ability to detect reticulate evolution in spruce species.
bioRxiv · Sep 14, 2017Evolutionary Biology