Journal
NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-22073-8
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Funding
- Data Science & AI Center at TAU
- Council for Higher Education program
- Tel Aviv University
- Rothchild Caesarea Foundation
- Edmond J. Safra Center for Bioinformatics at Tel Aviv University
- Israel Science Foundation (ISF) [993/17]
- Israel Science Foundation [961/17, 802/16]
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This study utilizes a machine learning algorithm to predict neighboring trees that increase likelihood, aiming to accelerate heuristic tree searches. Results suggest that machine learning can guide tree-search methodologies towards the most promising candidate trees.
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.
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