Information Metrics for Phylogenetic Trees via Distributions of Discrete and Continuous Characters

Maryam K. Garba, Tom M.W. Nye*, Jonas Lueg, Stephan F. Huckemann

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A wide range of metrics between phylogenetic trees are used in evolutionary molecular biology. They are typically based directly on the branching patterns and edge lengths represented by the trees. Metrics have recently been proposed which are based on the information content of distributions of genetic characters induced by the trees. We first show how these metrics lead to a change to the topology of the underlying tree space. Next we show via computational methods that the metrics are stable under changes to the Markov process used to generate characters, at least in the case of 5 taxa. As a result, a Gaussian process defined over the edges of trees can be used to compute the metrics, leading to a substantial computational efficiency over DNA nucleotide-valued Markov process models.

Original languageEnglish
Title of host publicationGeometric Science of Information
Subtitle of host publication5th International Conference, GSI 2021, Paris, France, July 21–23, 2021, Proceedings
EditorsFrank Nielsen, Frédéric Barbaresco
Place of PublicationCham, Switzerland
Number of pages9
ISBN (Electronic)9783030802097
ISBN (Print)9783030802080
Publication statusPublished - 10 Jul 2021
Externally publishedYes
Event5th International Conference on Geometric Science of Information, GSI 2021 - Paris, France
Duration: 21 Jul 202123 Jul 2021
Conference number: 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12829 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Geometric Science of Information, GSI 2021

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