21 papers · ranked by Valyu relevance
Lars Berling, Jonathan Klawitter, Remco Bouckaert, Dong Xie + 2 more
Bayesian phylogenetic analysis with MCMC algorithms generates an estimate of the posterior distribution of phylogenetic trees in the form of a sample of phylogenetic trees and related parameters. The high dimensionality and non-Euclidean nature of tree space complicates summarizing the central tendency and variance of…
Lars Berling, Jonathan Klawitter, Remco Bouckaert, Dong Xie + 3 more
'Alex Gavryushkin' 'Alexei J Drummond' 'Natalia L. Komarova'] Bayesian phylogenetic analysis with MCMC algorithms generates an estimate of the posterior distribution of phylogenetic trees in the form of a sample of phylogenetic trees and related parameters. The high dimensionality and non-Euclidean nature of tree space…
Ivan Specht, Julia A. Palacios
Bayesian phylogenetic inference from molecular sequences can provide key insights into the evolutionary history of populations. Existing tools, however, often scale poorly with sample size. We present inPhynite, a highly-efficient Bayesian phylogenetics algorithm for genomic datasets compatible with the infinite sites…
Jakub Truszkowski, Allison Perrigo, David Broman, Fredrik Ronquist + 2 more
'Alexandre Antonelli' 'Simon Ho'] Title: Abstract Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncertainty of inferences and the possibility of…
Joëlle Barido-Sottani, Orlando Schwery, Rachel C. M. Warnock, Chi Zhang + 1 more
'Chi Zhang' 'April Marie Wright'] Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model…
Adam Siepel, Rebecca Hassett, Stephen J. Staklinski
Bayesian phylogenetic inference is now widely used but remains heavily reliant on Markov chain Monte Carlo (MCMC) sampling, which is computationally intensive and requires careful convergence monitoring. Variational inference (VI) is an appealing alternative that approximates posterior distributions without sampling…
Patrick Varilly, Mark Schifferli, Katherine Yang, Tim Burcham + 21 more
Pathogen genomic analysis is central to tracking, understanding, and containing outbreaks, but complexity and high costs of state-of-the-art (SOTA) phylogenetic tools limit global access and impact. We introduce Delphy, an exact reformulation of Bayesian phylogenetics designed to transform its speed, scalability and…
Cheng Zhang, Frederick A. Matsen
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper, we present an alternative approach: a variational framework for Bayesian…
Matthew Macaulay, Aaron Darling, Mathieu Fourment, William Stafford Noble
'William Stafford Noble'] Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. However, Bayesian phylogenetics faces the challenging computational problem of moving throughout the high-dimensional space of trees. Fortunately, hyperbolic space offers a low dimensional…
Patricio Maturana‐Russel, Brendon J. Brewer, Steffen Klaere, Remco Bouckaert
'Remco Bouckaert'] Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This…
Jamie R Oaks, Kerry A Cobb, Vladimir N Minin, Adam D Leaché + 1 more
'Olivier Gascuel'] Title: Abstract By providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics is becoming the statistical foundation of biology. The importance of model choice continues to grow as phylogenetic models continue to increase in complexity to better capture micro- and…
Alexander A. Fisher, Gabriel W. Hassler, Xiang Ji, Guy Baele + 2 more
'Marc A. Suchard' 'Philippe Lemey'] Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a…
Matthew Macaulay, Aaron E. Darling, Mathieu Fourment
Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. It faces the challenging problem of moving throughout the high-dimensional space of trees. However, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences…
Liangliang Wang, Shijia Wang, Alexandre Bouchard‐Côté
We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo, based on recent advances in the Sequential Monte Carlo literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and…
Yaxuan Wang, Huw A. Ogilvie, Luay Nakhleh
Species tree inference from multi-locus data has emerged as a powerful paradigm in the post-genomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this…
Jamie R. Oaks, Kerry A. Cobb, Vladimir N. Minin, Adam D. Leaché
By providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics is becoming the statistical foundation of biology. The importance of model choice continues to grow as phylogenetic models continue to increase in complexity to better capture micro and macroevolutionary processes. In a…
Ziheng Yang, Tianqi Zhu
The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this over-confidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great…
Jérôme Eberhardt, Markus Lill, Torsten Schwede
This study introduces a novel Bayesian Optimization (BO) method to support the design and optimization of bioactive peptide sequences in the context of a fully automated closed-loop Design-Make-Test (DMT) pipeline. Using the major histocompatibility complex class I receptor system as test case, we showed that BO is…
Xinqiang Ding, John Drohan
A common approach for computing free energy differences among multiple states is to build a perturbation graph connecting the states and compute free energy differences on all edges of the graph. Such perturbation graphs are often designed to have cycles. Because free energy is a function of states, the free energy…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…