ArticleG3 (Bethesda, Md.)2021
Visualizing population structure with variational autoencoders.
Article in G3 (Bethesda, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Topological stratification of continuous genetic variation in large biobanks.PLoS genetics · 2026Article
- Autoencoders for genomic variation analysis.Genome research · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
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- Framework for assessing genetic variation in livestock using demographic, pedigree, and genomic measures.Frontiers in genetics · 2026Review
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- Genomics of Experimental Adaptive Radiation in the Cryptic Coloration of Feather Lice.Genome biology and evolution · 2025Article
- Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.PLoS computational biology · 2025Article
- Pandora: a tool to estimate dimensionality reduction stability of genotype data.Bioinformatics advances · 2025Article
- Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort.PloS one · 2025Article
- Disentangling cobionts and contamination in long-read genomic data using sequence composition.G3 (Bethesda, Md.) · 2024Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
- Tree sequences as a general-purpose tool for population genetic inference.bioRxiv : the preprint server for biology · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Microgeographic population structuring in a genus of California trapdoor spiders and discovery of an enigmatic new species (Euctenizidae:Ecology and evolution · 2024Article
- IntroUNET: Identifying introgressed alleles via semantic segmentation.PLoS genetics · 2024Article
- IntroUNET: identifying introgressed alleles via semantic segmentation.bioRxiv : the preprint server for biology · 2024Article
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3 authors.
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Abstract
Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs)-generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data-for visualizing population genetic variation. VAEs incorporate nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.
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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.