Harnessing machine learning to guide phylogenetic-tree search algorithms

Dana Azouri, Shiran Abadi, Yishay Mansour, Itay Mayrose () and Tal Pupko ()
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Dana Azouri: Tel Aviv University, Ramat Aviv
Shiran Abadi: Tel Aviv University, Ramat Aviv
Yishay Mansour: Tel-Aviv University, Ramat Aviv
Itay Mayrose: Tel Aviv University, Ramat Aviv
Tal Pupko: Tel Aviv University, Ramat Aviv

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-22073-8

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