Harnessing machine learning to guide phylogenetic-tree search algorithms

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, inferen...

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Vydáno v:	Nature communications Ročník 12; číslo 1; s. 1983 - 9
Hlavní autoři:	Azouri, Dana, Abadi, Shiran, Mansour, Yishay, Mayrose, Itay, Pupko, Tal
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 31.03.2021 Nature Publishing Group Nature Portfolio
Témata:	631/114/1305 631/114/2397 631/181/735 631/181/757 Accuracy Algorithms Animals Artificial intelligence Computer applications Computing time Databases, Genetic - statistics & numerical data Databases, Protein - statistics & numerical data Empirical analysis Evolution, Molecular Humanities and Social Sciences Humans Inference Learning algorithms Machine Learning Models, Genetic multidisciplinary Optimization Phylogenetics Phylogeny Reconstruction Run time (computers) Science Science (multidisciplinary) Search algorithms Sequences Trees
ISSN:	2041-1723, 2041-1723
On-line přístup:	Získat plný text
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Shrnutí:	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 accuracy and running time. In this proof-of-concept study, we train a machine-learning algorithm over an extensive cohort of empirical data to predict the neighboring trees that increase the likelihood, without actually computing their likelihood. This provides means to safely discard a large set of the search space, thus potentially accelerating heuristic tree searches without losing accuracy. Our analyses suggest that machine learning can guide tree-search methodologies towards the most promising candidate trees. 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-021-22073-8