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Phylo2Vec: A Vector Representation for Binary Trees.

Matthew J Penn1, Neil Scheidwasser2, Mark P Khurana2

  • 1Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK.

Systematic Biology
|June 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Phylo2Vec offers a novel integer vector encoding for binary phylogenetic trees, simplifying manipulation and representation. This method enables faster tree sampling, compressed storage, and efficient traversal of evolutionary tree space for machine learning inference.

Keywords:
Binary treesoptimizationphylogeneticsrepresentation

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Phylogenetic trees are crucial for understanding evolutionary history.
  • Inferring tree structures, especially latent nodes, is computationally intensive.
  • Current methods use heuristics and specific data structures (e.g., Newick strings) for tree manipulation and representation.

Purpose of the Study:

  • To introduce Phylo2Vec, a unified and parsimonious encoding for binary phylogenetic trees.
  • To demonstrate Phylo2Vec's utility in simplifying tree manipulation, representation, and analysis.
  • To showcase Phylo2Vec's effectiveness in machine learning-based phylogenetic inference.

Main Methods:

  • Developed Phylo2Vec, mapping binary trees with n leaves to unique integer vectors of length n-1.
  • Utilized Phylo2Vec for compressed tree representation and topological verification.
  • Employed Phylo2Vec within a hill-climbing optimization scheme for tree space traversal.
  • Applied the method to 5 real-world datasets for machine learning inference.

    • Main Results:

    • Phylo2Vec provides a fast and compressed representation of phylogenetic trees.
    • The encoding allows for quick and unambiguous topological comparison of trees.
    • Phylo2Vec facilitates efficient traversal of the tree space for optimization.
    • Demonstrated successful application in machine learning inference, moving from random to optimal trees.

    Conclusions:

    • Phylo2Vec offers a significant advancement in handling and representing phylogenetic trees.
    • The encoding unifies manipulation and representation, overcoming computational challenges.
    • Phylo2Vec shows promise for accelerating machine learning applications in phylogenetics.