ArticleNature computational science2023
Neural ADMIXTURE for rapid genomic clustering.
Article in Nature computational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 44 citations in OpenAlex.
- The Landscape of Genomic and Socioeconomic Variables in Patients with Colorectal Cancer Based on Genetic Ancestry.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Analysis of Inbreeding, Population Structure, and Genetic Diversity in the Kumamoto Sub-Breed of Japanese Brown Cattle.Animals : an open access journal from MDPI · 2026Article
- HPRC2: A human pangenome reference with near-complete coverage of common genetic variation.bioRxiv : the preprint server for biology · 2026Article
- ADAMIXTURE: adaptive first-order optimization for biobank-scale genetic clustering.Bioinformatics (Oxford, England) · 2026Article
- Non-Parametric Ancestry Adjustment for Polygenic Scores.medRxiv : the preprint server for health sciences · 2026Article
- Autoencoders for genomic variation analysis.Genome research · 2026Article
- Integrated multi-omics profiling identifies genetic loci of African swine fever resistance in pigs.GigaScience · 2026Article
- Framework for assessing genetic variation in livestock using demographic, pedigree, and genomic measures.Frontiers in genetics · 2026Review
- PopCluster Improves Accessibility, Speed and Accuracy of Available Genotypic Clustering Software.Molecular ecology resources · 2025Article
- Revealing the range of equally likely estimates in the admixture model.G3 (Bethesda, Md.) · 2025Article
- Sparse haplotype-based fine-scale local ancestry inference at scale reveals recent selection on immune responses.Nature communications · 2025Article
- Inferring ancestry with the hierarchical soft clustering approach tangleGen.Genome research · 2024Article
- Deep learning insights into distinct patterns of polygenic adaptation across human populations.Nucleic acids research · 2024Article
- A genotyping array for the globally invasive vector mosquito, Aedes albopictus.Parasites & vectors · 2024Article
- A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China.PloS one · 2024Article
- Harnessing deep learning for population genetic inference.Nature reviews. Genetics · 2024Review
- Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.bioRxiv : the preprint server for biology · 2023Article
- Deep Learning in Population Genetics.Genome biology and evolution · 2023Article
- Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research.Human genomics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by calculating multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.
Identifiers
What OpenQuestion holds
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.