In this section
@article{Penn:2024:sysbio/syae030,
author = {Penn, MJ and Scheidwasser, N and Khurana, MP and Duchêne, DA and Donnelly, CA and Bhatt, S},
doi = {sysbio/syae030},
journal = {Syst Biol},
title = {Phylo2Vec: a vector representation for binary trees.},
url = {http://dx.doi.org/10.1093/sysbio/syae030},
year = {2024}
}
TY - JOUR
AB - Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search, using different data structures for easy manipulation (e.g., classes in object-oriented programming languages) and readable representation of trees (e.g., Newick-format strings). Here, we present Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a unified approach for both manipulating and representing phylogenetic trees. Phylo2Vec maps any binary tree with n leaves to a unique integer vector of length n - 1. The advantages of Phylo2Vec are fourfold: i) fast tree sampling, (ii) compressed tree representation compared to a Newick string, iii) quick and unambiguous verification if two binary trees are identical topologically, and iv) systematic ability to traverse tree space in very large or small jumps. As a proof of concept, we use Phylo2Vec for maximum likelihood inference on five real-world datasets and show that a simple hill-climbing-based optimisation scheme can efficiently traverse the vastness of tree space from a random to an optimal tree.
AU - Penn,MJ
AU - Scheidwasser,N
AU - Khurana,MP
AU - Duchêne,DA
AU - Donnelly,CA
AU - Bhatt,S
DO - sysbio/syae030
PY - 2024///
TI - Phylo2Vec: a vector representation for binary trees.
T2 - Syst Biol
UR - http://dx.doi.org/10.1093/sysbio/syae030
UR - https://www.ncbi.nlm.nih.gov/pubmed/38935520
ER -
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