Evidence map›Paper›PMID 41848929›Full record

ArticleBulletin of mathematical biology2026

Vector Encoding of Phylogenetic Trees by Ordered Leaf Attachment.

David Harry Richman, Cheng Zhang, Frederick A Matsen

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

David Harry RichmanComputational Biology Program, Fred Hutchinson Cancer Center, Seattle, 98109, Washington, USA. hrichman@ncts.ntu.edu.tw.ORCID http://orcid.org/0000-0002-0101-0521
Cheng ZhangSchool of Mathematical Sciences and Center for Statistical Science, Peking University, Haidian District, 100871, Beijing, China.
Frederick A MatsenComputational Biology Program, Fred Hutchinson Cancer Center, Seattle, 98109, Washington, USA.

Funding

Fast and flexible Bayesian phylogenetics via modern machine learningR01AI162611 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2021 to 2025
$3.8M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
NIAID NIH HHS R01 AI162611NIH HHS AI162611NIH HHS S10 OD028685Office of Research Infrastructure Programs, National Institutes of Health S10OD028685
6 · The paper itself

Abstract

As part of work to connect phylogenetics with machine learning, there has been considerable recent interest in vector encodings of phylogenetic trees. We present a simple new "ordered leaf attachment" (OLA) method for uniquely encoding a binary, rooted phylogenetic tree topology as an integer vector. OLA encoding and decoding take linear time in the number of leaf nodes, and the set of vectors corresponding to trees is a simply-described subset of integer sequences. The OLA encoding is unique compared to other existing encodings in having these properties. The integer vector encoding induces a distance on the set of trees, and we investigate this distance in relation to the NNI and SPR distances.

Indexed as

Models, GeneticPhylogenyAlgorithmsComputational BiologyMachine LearningMathematical ConceptsBinary treePhylogeneticsTree encoding

Identifiers

PMID41848929
PMCPMC12999618

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.