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Found 15 papers, 13 papers with code
Variational Bayesian Phylogenetic Inference with Semi-implicit Branch Length Distributions
Reconstructing the evolutionary history relating a collection of molecular sequences is the main subject of modern Bayesian phylogenetic inference.
On the importance of assessing topological convergence in Bayesian phylogenetic inference
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
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.
Automatic differentiation is no panacea for phylogenetic gradient computation
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning.
A Variational Approach to Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms.
Inference of B cell clonal families using heavy/light chain pairing information
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.
Using B cell receptor lineage structures to predict affinity
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
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
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.
19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology
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
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
Probability estimation is one of the fundamental tasks in statistics and machine learning.
Applications
Using genotype abundance to improve phylogenetic inference
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
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
The subtree prune-and-regraft (SPR) distance metric is a fundamental way of comparing evolutionary trees.
Data Structures and Algorithms Populations and Evolution
