Phylogenetic inference using generative adversarial networks.

ABSTRACT:

Motivation

The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, 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 i

SUBMITTER: Smith ML 

PROVIDER: S-EPMC10500083 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature