ArticleNature communications2019
Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Scalable ARG-free Detection of Denisovan-mediated Superarchaic Introgression Reveals Heterogeneous Patterns across Populations.bioRxiv : the preprint server for biology · 2026Article
- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.bioRxiv : the preprint server for biology · 2026Article
- Buried Treasure? Overlooked and Newly Discovered Evolutionary Contributions to Human Brain Diseases.Annals of neurology · 2025Review
- Resolving out of Africa event for Papua New Guinean population using neural network.Nature communications · 2025Article
- Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data.Heredity · 2025Article
- Genomic regions of current low hybridisation mark long-term barriers to gene flow in scarce swallowtail butterflies.PLoS genetics · 2025Article
- Population genomics advances in frontier ethnic minorities in China.Science China. Life sciences · 2025Review
- Computational Genomics and Its Applications to Anthropological Questions.American journal of biological anthropology · 2024Review
- Uncovering ghost introgression through genomic analysis of a distinct eastern Asian hickory species.The Plant journal : for cell and molecular biology · 2024Article
- Modelling the demographic history of human North African genomes points to a recent soft split divergence between populations.Genome biology · 2024Article
- Denisovan admixture facilitated environmental adaptation in Papua New Guinean populations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.Molecular biology and evolution · 2024Article
- Computationally efficient demographic history inference from allele frequencies with supervised machine learning.bioRxiv : the preprint server for biology · 2024Article
- Harnessing deep learning for population genetic inference.Nature reviews. Genetics · 2024Review
- Temporal Variation in Introgressed Segments' Length Statistics Computed from a Limited Number of Ancient Genomes Sheds Light on Past Admixture Pulses.Molecular biology and evolution · 2023Article
- Comparative physiological anthropogeny: exploring molecular underpinnings of distinctly human phenotypes.Physiological reviews · 2023Review
- Deep Learning in Population Genetics.Genome biology and evolution · 2023Article
- Review
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3 authors.
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Abstract
Since anatomically modern humans dispersed Out of Africa, the evolutionary history of Eurasian populations has been marked by introgressions from presently extinct hominins. Some of these introgressions have been identified using sequenced ancient genomes (Neanderthal and Denisova). Other introgressions have been proposed for still unidentified groups using the genetic diversity present in current human populations. We built a demographic model based on deep learning in an Approximate Bayesian Computation framework to infer the evolutionary history of Eurasian populations including past introgression events in Out of Africa populations fitting the current genetic evidence. In addition to the reported Neanderthal and Denisovan introgressions, our results support a third introgression in all Asian and Oceanian populations from an archaic population. This population is either related to the Neanderthal-Denisova clade or diverged early from the Denisova lineage. We propose the use of deep learning methods for clarifying situations with high complexity in evolutionary genomics.
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