Harnessing machine learning to guide phylogenetic-tree search algorithms.
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ABSTRACT:
SUBMITTER: Azouri D
PROVIDER: S-EPMC8012635 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
Publications
Harnessing machine learning to guide phylogenetic-tree search algorithms.
Azouri Dana D Abadi Shiran S Mansour Yishay Y Mayrose Itay I Pupko Tal T
Nature communications 20210331 1
Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems involving more than a handful of sequences, inference under the maximum-likelihood paradigm integrates heuristic approaches to evaluate only a subset of all potential trees. Consequently, existing methods suffer from the known tradeoff between accurac ...[more]
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