Evaluating Branch Swapping Methods for Topology Search in Machine Learning-Augmented Phylogenetic Tree Inference

DOI

10.2991/978-94-6239-638-8_19How to use a DOI?

Keywords

phylogenetic tree inference; tree topology search; branch swapping; machine learning

Abstract

Methods for inferring phylogenetic trees such as maximum likelihood-based methods face scalability challenges due to the computational cost of evaluating candidate trees. To address this, the study evaluates the potential of integrating machine learning models with branchswapping heuristics for guiding tree search. We assess model performance based on Spearman correlation with true likelihood rankings, as well as the relative position of the predicted best neighbor within the empirical ranking, and vice versa. Our results highlight the potential of machine learning-guided heuristics to enhance the efficiency and accuracy of phylogenetic tree inference, and extend prior work by comparing multiple heuristics beyond Subtree Pruning and Regrafting.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jan Michael Yap
AU  - Eugene Kasilag
AU  - Camille Comia
PY  - 2026
DA  - 2026/04/30
TI  - Evaluating Branch Swapping Methods for Topology Search in Machine Learning-Augmented Phylogenetic Tree Inference
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2025)
PB  - Atlantis Press
SP  - 387
EP  - 402
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-638-8_19
DO  - 10.2991/978-94-6239-638-8_19
ID  - Yap2026
ER  -