Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments

Machine learning can be as good as maximum likelihood when reconstructing
phylogenetic topologies and determining the best evolutionary model on
four taxon alignments. Phylogenetic tree reconstruction with molecular
data is important in many fields of life science research. The gold
standard in this discipline is the Maximum Likelihood tree reconstruction
method. Here we show that for quartet trees, Machine Learning using neural
networks can be as good as the Maximum Likelihood method to infer the best
tree topology and the best model of sequence evolution for nucleotide as
well as amino acid sequences. For this purpose we simulated data sets for
a wide range of branch lengths, evolutionary models and model parameters
and compared the topologies and inferred models obtained with Machine
learning with those obtained with the Maximum Likelihood and the Neighbour
Joining method. Our results show that neural networks are a promising
avenue for determining relatedness between taxa, which is likely to
accelerate the construction of phylogenetic trees in the future, while
maintaining a high accuracy.