Search Results for author: Frederick A. Matsen IV

Found 14 papers, 12 papers with code

On the importance of assessing topological convergence in Bayesian phylogenetic inference

no code implementations18 Feb 2024 Marius Brusselmans, Luiz Max Carvalho, Samuel L. Hong, Jiansi Gao, Frederick A. Matsen IV, Andrew Rambaut, Philippe Lemey, Marc A. Suchard, Gytis Dudas, Guy Baele

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution.

Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph

1 code implementation11 Oct 2023 Will Dumm, Mary Barker, William Howard-Snyder, William S. DeWitt, Frederick A. Matsen IV

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees.

Uncertainty Quantification

Automatic differentiation is no panacea for phylogenetic gradient computation

2 code implementations3 Nov 2022 Mathieu Fourment, Christiaan J. Swanepoel, Jared G. Galloway, Xiang Ji, Karthik Gangavarapu, Marc A. Suchard, Frederick A. Matsen IV

Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning.

Variational Inference

A Variational Approach to Bayesian Phylogenetic Inference

1 code implementation16 Apr 2022 Cheng Zhang, Frederick A. Matsen IV

Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms.

Efficient Exploration Variational Inference

Inference of B cell clonal families using heavy/light chain pairing information

1 code implementation21 Mar 2022 Duncan K. Ralph, Frederick A. Matsen IV

Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR.

Clustering

Using B cell receptor lineage structures to predict affinity

2 code implementations24 Apr 2020 Duncan K. Ralph, Frederick A. Matsen IV

In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen.

A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis

1 code implementation27 Jun 2019 Amrit Dhar, Duncan K. Ralph, Vladimir N. Minin, Frederick A. Matsen IV

Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process.

Methodology Genomics Applications

Variational Bayesian Phylogenetic Inference

no code implementations ICLR 2019 Cheng Zhang, Frederick A. Matsen IV

Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo with simple mechanisms for proposing new states, which hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates.

Variational Inference

19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology

1 code implementation28 Nov 2018 Mathieu Fourment, Andrew F. Magee, Chris Whidden, Arman Bilge, Frederick A. Matsen IV, Vladimir N. Minin

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model.

Non-bifurcating phylogenetic tree inference via the adaptive LASSO

1 code implementation28 May 2018 Cheng Zhang, Vu Dinh, Frederick A. Matsen IV

Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system.

Generalizing Tree Probability Estimation via Bayesian Networks

1 code implementation NeurIPS 2018 Cheng Zhang, Frederick A. Matsen IV

Probability estimation is one of the fundamental tasks in statistics and machine learning.

Applications

Using genotype abundance to improve phylogenetic inference

1 code implementation29 Aug 2017 William S. DeWitt III, Luka Mesin, Gabriel D. Victora, Vladimir N. Minin, Frederick A. Matsen IV

Modern biological techniques enable very dense genetic sampling of unfolding evolutionary histories, and thus frequently sample some genotypes multiple times.

Probabilistic Path Hamiltonian Monte Carlo

3 code implementations ICML 2017 Vu Dinh, Arman Bilge, Cheng Zhang, Frederick A. Matsen IV

Hamiltonian Monte Carlo (HMC) is an efficient and effective means of sampling posterior distributions on Euclidean space, which has been extended to manifolds with boundary.

Calculating the Unrooted Subtree Prune-and-Regraft Distance

3 code implementations24 Nov 2015 Chris Whidden, Frederick A. Matsen IV

The subtree prune-and-regraft (SPR) distance metric is a fundamental way of comparing evolutionary trees.

Data Structures and Algorithms Populations and Evolution

Cannot find the paper you are looking for? You can Submit a new open access paper.