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Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization.

Anastasis Togkousidis1,2,3, Alexandros Stamatakis1,2,4, Olivier Gascuel3

  • 1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Schloß-Wolfsbrunnenweg 35, Heidelberg 69118, Germany.

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|May 30, 2025
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Summary
This summary is machine-generated.

Phylogenetic inference tools now feature early stopping criteria using the Kishino-Hasegawa (KH) test to prevent overoptimization. This method significantly speeds up analysis while maintaining tree accuracy for DNA and protein data.

Keywords:
Early Stoppingmaximum likelihoodstopping criteria

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • Maximum likelihood-based phylogenetic inference is an optimization problem prone to overoptimization and overfitting due to noisy sequence data.
  • Existing methods may excessively optimize, leading to computational inefficiency and potentially inaccurate evolutionary models.
  • There is a need for reliable early stopping criteria to balance optimization thoroughness with computational cost and data noise.

Purpose of the Study:

  • To integrate the Kishino-Hasegawa (KH) test as an early stopping criterion into RAxML-NG to prevent overoptimization.
  • To develop a simplified heuristic tree search strategy (sRAxML-NG) as a foundation for the early stopping method.
  • To propose an extension of the KH test for multiple testing correction to enhance speed and accuracy.

Main Methods:

  • Implemented a simplified heuristic tree search strategy (sRAxML-NG) within RAxML-NG.
  • Integrated the Kishino-Hasegawa (KH) test to statistically assess improvements between intermediate phylogenetic trees.
  • Developed and applied a multiple testing correction extension to the KH test for enhanced performance.
  • Benchmarked performance using 300 empirical DNA and amino acid (AA) datasets from TreeBASE.

Main Results:

  • Early stopping methods using KH test and sRAxML-NG achieved statistically equivalent trees to RAxML-NG v1.2 for 98% of DNA datasets.
  • For AA datasets, sRAxML-NG, KH, and KH-multiple testing versions yielded statistically equivalent trees in 96%, 95%, and 92% of cases, respectively.
  • The KH-multiple testing version with sRAxML-NG provided average speedups of 5× for DNA and 3.9× for protein datasets compared to RAxML-NG v1.2.

Conclusions:

  • The implemented early stopping criteria, particularly the KH test with multiple testing correction, effectively prevent overoptimization in phylogenetic inference.
  • These methods offer significant computational speedups without compromising the statistical accuracy of inferred phylogenetic trees.
  • The early stopping criteria are now integrated into RAxML-NG, providing a more efficient tool for phylogenetic analysis.