Many-core algorithms for high-dimensional gradients on phylogenetic trees.

ABSTRACT:

Motivation

Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling r

SUBMITTER: Gangavarapu K 

PROVIDER: S-EPMC10868298 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature