MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.

ABSTRACT: The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center fo

SUBMITTER: Berman DS 

PROVIDER: S-EPMC10104372 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature