Harnessing machine learning to guide phylogenetic-tree search algorithms

Nat Commun. 2021 Mar 31;12(1):1983. doi: 10.1038/s41467-021-22073-8.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Animals
  • Databases, Genetic / statistics & numerical data
  • Databases, Protein / statistics & numerical data
  • Evolution, Molecular*
  • Humans
  • Machine Learning*
  • Models, Genetic
  • Phylogeny*