ArticlePLoS genetics2023
Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.
Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 32 citations in OpenAlex.
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- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging-Based Cognitive Prediction.bioRxiv : the preprint server for biology · 2026Article
- Haplotype-Based Models Improve Sweep Detection in Ancient Populations with Complex Demography.bioRxiv : the preprint server for biology · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- The persistence and loss of hard selective sweeps amid admixture in ancient Eurasians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Graph transformer for ancient ancestry inference.bioRxiv : the preprint server for biology · 2026Article
- Identifying Adaptive Footprints in the Presence of Demographic Uncertainty.Genome biology and evolution · 2026Article
- Soft Selective Sweeps Predominate in the Yellow Fever Mosquito Aedes aegypti.Molecular biology and evolution · 2026Article
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.bioRxiv : the preprint server for biology · 2026Article
- Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.bioRxiv : the preprint server for biology · 2026Article
- Recent advances in methods to characterize archaic introgression in modern humans.Genome research · 2026Review
- Multiple-testing corrections in selection scans using identity-by-descent segments.American journal of human genetics · 2025Article
- The persistence and loss of hard selective sweeps amid ancient human admixture.bioRxiv : the preprint server for biology · 2025Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- Signatures of soft selective sweeps predominate in the yellow fever mosquitobioRxiv : the preprint server for biology · 2025Article
- Parameter Scaling in Population Genetics Simulations may Introduce Unintended Background Selection: Considerations for Scaled Simulation Design.Genome biology and evolution · 2025Article
- Multiple-testing corrections in selection scans using identity-by-descent segments.bioRxiv : the preprint server for biology · 2025Article
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2 authors at 1 institution in 1 country.
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Abstract
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.
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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.