ArticleVirus evolution2023
MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.
Article in Virus evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery.PLoS computational biology · 2026Article
- SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein.Briefings in bioinformatics · 2025Article
- Synthetic data generation: a privacy-preserving approach to accelerate rare disease research.Frontiers in digital health · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Article
- Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision.ArXiv · 2024Article
- Understanding the natural language of DNA using encoder-decoder foundation models with byte-level precision.Bioinformatics advances · 2024Article
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- Permafrost viremia and immune tweening.Bioinformation · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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 for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.