Harnessing machine learning to guide phylogenetic-tree search algorithms

Likelihood optimization in phylogenetic tree reconstruction is computationally intensive, especially as the number of sequences and taxa included increase. Here, Azouri et al. show how an artificial intelligence approach can reduce computational time without losing accuracy of tree inference.

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Bibliographic Details
Main Authors: Dana Azouri, Shiran Abadi, Yishay Mansour, Itay Mayrose, Tal Pupko
Format: article
Language:EN
Published: Nature Portfolio 2021
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Online Access:https://doaj.org/article/1bdcf3cd732e45cb83b0599ce656b10d
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Summary:Likelihood optimization in phylogenetic tree reconstruction is computationally intensive, especially as the number of sequences and taxa included increase. Here, Azouri et al. show how an artificial intelligence approach can reduce computational time without losing accuracy of tree inference.