Phylogenetic inference using Generative Adversarial Networks

Motivation The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. In particular, supervised machine learning approaches require data from across this space to train models. Because of this requirement, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences, and, since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning to inferring phylogenetic relationships. Results We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and then infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. phyloGAN performs well for up to ten taxa in the concatenation case, and performs well for six taxa when considering gene tree heterogeneity. Future developments to the network should aim to make the exploration of this more complex model space more efficient to reduce run times and to enable the consideration of larger trees. Availability phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/. Contact mls16@indiana.edu