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

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 co

SUBMITTER: Togkousidis A 

PROVIDER: S-EPMC12712337 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature