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June 13, 2026BMC BioinformaticsOpen Access

Practical phylogenetic usage of theoretical advances in distance-based tree learning

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Authors

AKAnastasiia KimLos Alamos National LaboratoryALAndrey Y. LokhovLos Alamos National LaboratoryMVMarc VuffrayLos Alamos National Laboratory

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Implication

Randomized trial assesses new algorithm for phylogeny inference, suggesting improvements for biological research.

Key Points

  • This research aims to implement and evaluate a fast-converging algorithm for phylogeny inference, particularly in scenarios with short sequence lengths.
  • Simulation study evaluating the algorithm's performance on short sequence lengths.
  • Comparison of algorithm predictions against actual biological relationships.
  • Deployment guidance provided for use when true phylogenetic trees are unknown.
  • The algorithm returned correct relationships for short sequence lengths but needed longer sequences for well-resolved trees.
  • Realistic datasets often didn't meet algorithm assumptions but still produced mostly correct trees.
  • However, the correctness of returned trees was compromised, affecting their resolution.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf604faef96ed7f057e54https://doi.org/10.1186/s12859-026-06488-y
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Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Number of Evolutionary Trees1978 · 558 citations
  2. 2Evolutionary trees and the Ising model on the Bethe lattice: a proof of Steel’s conjecture2009 · 73 citations
  3. 3Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations2023 · 24 citations