Deep learning from phylogenies for diversification analyses

Birth-death models are widely used in combination with species phylogenies
to study past diversification dynamics. Current inference approaches
typically rely on likelihood-based methods. These methods are not
generalizable, as a new likelihood formula must be established each time a
new model is proposed; for some models, such a formula is not even
tractable. Deep learning can bring solutions in such situations, as deep
neural networks can be trained to learn the relation between simulations
and parameter values as a regression problem. In this paper, we adapt a
recently developed deep learning method from pathogen phylodynamics to the
case of diversification inference, and we extend its applicability to the
case of the inference of state-dependent diversification models from
phylogenies associated with trait data. We demonstrate the accuracy and
time efficiency of the approach for the time-constant homogeneous
birth-death model and the Binary-State Speciation and Extinction model.
Finally, we illustrate the use of the proposed inference machinery by
reanalyzing a phylogeny of primates and their associated ecological role
as seed dispersers. Deep learning inference provides at least the same
accuracy as likelihood-based inference while being faster by several
orders of magnitude, offering a promising new inference approach for
deployment of future models in the field.