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

Togkousidis, Anastasis 1; Stamatakis, Alexandros ORCID iD icon 2; Gascuel, Olivier; Thomson, Robert [Hrsg.]
1 Karlsruher Institut für Technologie (KIT)
2 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)

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. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000185670
Veröffentlicht am 13.10.2025
Originalveröffentlichung
DOI: 10.1093/sysbio/syaf043
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Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2025
Sprache Englisch
Identifikator ISSN: 1063-5157, 1076-836X
KITopen-ID: 1000185670
Erschienen in Systematic Biology
Verlag Oxford University Press (OUP)
Vorab online veröffentlicht am 30.05.2025
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