ArticleNAR genomics and bioinformatics2025
Generating realistic artificial human genomes using adversarial autoencoders.
Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- GPC: An expressive and tractable deep generative model for genetic variation data.PLoS genetics · 2026Article
- Generating synthetic genotypes using diffusion models.Bioinformatics (Oxford, England) · 2025Article
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Authors and funding
3 authors.
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Abstract
A publicly available human genome is both valuable to researchers and a risk for its donor. Many actors could exploit it to extract information about the donor's health or that of their relatives. Recent efforts have employed artificial intelligence models to simulate genomic data, aiming to create synthetic datasets with scientific merit while preserving patient anonymity. Challenges arise due to the vast amount of data that constitute a complete human genome and the computational resources required. We present a dimension reduction method that combines artificial intelligence with our knowledge of
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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.