October 3, 2025Open Access

Accurate and efficient phylogenetic inference through end-to-end deep learning

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Novel deep learning method enhances phylogenetic inference accuracy and efficiency in diverse taxa.

NeuralNJ improves phylogenetic inference accuracy compared to traditional methods, enhancing understanding of evolution.
By utilizing an end-to-end framework, it effectively reconstructs trees without disjoint stages, avoiding errors.
The method incorporates reinforcement learning for tree search, boosting computational efficiency significantly.
Empirical data showcases its application to hundreds of taxa, indicating potential for broader evolutionary studies.

Zhang et al. (2025) studied this question.

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