ArticleBioinformatics (Oxford, England)2025
New heuristics for phylogeny estimation under the balanced minimum evolution criterion.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Phylogenetic inference under the balanced minimum evolution criterion via semidefinite programming.Bioinformatics (Oxford, England) · 2026Article
- Learning to explore tree neighbourhoods for phylogenetic inference.Briefings in bioinformatics · 2026Article
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Authors and funding
3 authors.
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Abstract
Recent advances in the combinatorics of the Balanced Minimum Evolution Problem (BMEP) enabled the characterization of the mathematical properties that a symmetric integer matrix of order n≥3 must satisfy to encode the Path-Length Matrix of an Unrooted Binary Tree. This result, together with the identification of fundamental facet-defining inequalities for the convex hull of BMEP solutions, has led to an integer linear programming formulation that currently serves as the reference exact solution algorithm. Here, we show how to exploit these advances to improve the approximation algorithms for the problem. We first leverage the tight linear programming relaxation of this formulation to develop an enhanced Neighbor Joining-like heuristic. Next, we embed this heuristic into a Beam Search framework to further improve the quality of the solutions. Computational experiments show that the proposed algorithms outperform existing heuristics, making their use highly desirable in practice. AVAILABILITY AND IMPLEMENTATION: Codes and data are available at https://github.com/HenriDeh/BME_BeamSearch.git and archived at https://zenodo.org/records/15631441 (DOI: 10.5281/zenodo.15631440).
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