ArticlePLoS genetics2021
Creating artificial human genomes using generative neural networks.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
62 citing papers in PubMed, 130 citations in OpenAlex.
- GPC: An expressive and tractable deep generative model for genetic variation data.PLoS genetics · 2026Article
- PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data.Bioinformatics (Oxford, England) · 2026Article
- On the use of generative models for demographic inference in malaria vectors from genomic data.G3 (Bethesda, Md.) · 2026Article
- Privacy-preserving verification of preprocessing in federated learning for genomic data.JAMIA open · 2026Article
- Autoencoders for genomic variation analysis.Genome research · 2026Article
- Learning inherent genetic patterns and trait associations with deep generative models for discrete genotype simulation.GigaScience · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- A simple demonstration of a privacy-preserving de-centralised genotype imputation workflow.PloS one · 2026Article
- BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026Article
- PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2026Article
- A Novel Integrative Framework for Depression: Combining Network Pharmacology, Artificial Intelligence, and Multi-Omics with a Focus on the Microbiota-Gut-Brain Axis.Current issues in molecular biology · 2025Review
- Privacy-hardened and hallucination-resistant synthetic data generation with logic-solvers.Bioinformatics (Oxford, England) · 2025Article
- codonGPT: reinforcement learning on a generative language model enables scalable mRNA design.Nucleic acids research · 2025Article
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Article
- Generating realistic artificial human genomes using adversarial autoencoders.NAR genomics and bioinformatics · 2025Article
- Generating synthetic genotypes using diffusion models.Bioinformatics (Oxford, England) · 2025Article
- Conservation Evolutionary Biology: A Unified Framework Connecting the Past, Present, and Future of Biodiversity Conservation.Molecular biology and evolution · 2025Review
- The design and engineering of synthetic genomes.Nature reviews. Genetics · 2025Review
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.
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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.