Uma abordagem de aprendizado de máquina para inferência de árvore filogenética

terça-feira, abril 06, 2021

Harnessing machine learning to guide phylogenetic-tree search algorithms

Dana Azouri, Shiran Abadi, Yishay Mansour, Itay Mayrose & Tal Pupko

Nature Communications volume 12, Article number: 1983 (2021)


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.