Evidence map›Paper›PMID 40445803›Full record

ArticleSystematic biology2025

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

Anastasis Togkousidis, Alexandros Stamatakis, Olivier Gascuel

Abstract read
In one paragraph

Article in Systematic biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anastasis TogkousidisComputational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Schloß-Wolfsbrunnenweg 35, Heidelberg 69118, Germany.ORCID 0000-0003-4306-3709
Alexandros StamatakisComputational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Schloß-Wolfsbrunnenweg 35, Heidelberg 69118, Germany.ORCID 0000-0003-0353-0691
Olivier GascuelInstitut de Systématique, Evolution, Biodiversité (ISYEB, UMR7205-CNRS, Muséum National d'Histoire Naturelle, SU, EPHE, UA), 75005 Paris, France.ORCID 0000-0002-9412-9723

Funding

European Union 101087081German Research Foundation 35/1597-1Klaus Tschira FoundationPRAIRIE ANR-19-P3IA-0001
6 · The paper itself

Abstract

Maximum likelihood-based phylogenetic inference constitutes a challenging optimization problem. Given a set of aligned input sequences, phylogenetic inference tools strive to determine the tree topology, the branch lengths, and the evolutionary model parameters that maximize the phylogenetic likelihood function. However, there exist compelling reasons to not push optimization to its limits, by means of early, yet adequate stopping criteria. Because input sequences are typically subject to stochastic and systematic noise, caution is warranted to prevent overoptimization and the risk of overfitting the model to noisy data. To address this, we integrate the Kishino-Hasegawa (KH) test into RAxML-NG as a reliable and fast-to-compute Early Stopping criterion to effectively limit excessive and compute-intensive overoptimization. Initially, we introduce a simplified heuristic tree search strategy in RAxML-NG (sRAxML-NG) as an underlying method for Early Stopping. Subsequently, we use the KH test in combination with sRAxML-NG to statistically assess the significance of differences between intermediate trees prior to and after major optimization steps. The tree search terminates early when improvements are statistically insignificant. We also propose an extension to the standard KH test that allows to correct for multiple testing, which maintains accuracy while achieving even higher speedups. For benchmarking, we use 300 large representative empirical data sets from TreeBASE. For 98% of the DNA data sets, all Early Stopping methods we introduce infer trees that are statistically equivalent to those inferred from RAxML-NG v1.2. For AA data sets, the fraction of data sets where sRAxML-NG, KH, and the KH-multiple testing versions infer statistically equivalent trees is 96%, 95%, and 92%, respectively. In conjunction with sRAxML-NG, the average speedup achieved by the KH-multiple testing version is 5× for DNA and 3.9× for protein data sets compared with RAxML-NG v1.2. We implemented our stopping criteria in RAxML-NG, which is available under GNU GPL at https://github.com/togkousa/raxml-ng/tree/stopping-criteria.

Indexed as

ClassificationPhylogenyLikelihood FunctionsEarly Stoppingmaximum likelihoodstopping criteria

Identifiers

PMID40445803
PMCPMC12712337

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.