ArticleGenome biology and evolution2023
Deep Learning in Population Genetics.
Article in Genome biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Gene tree patterns help answer: vicariance or dispersal?bioRxiv : the preprint server for biology · 2026Article
- Genomic insights into natural selection in recent human history.Nature reviews. Genetics · 2026Review
- On the use of generative models for demographic inference in malaria vectors from genomic data.G3 (Bethesda, Md.) · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Discriminating models of trait evolution.Evolution; international journal of organic evolution · 2026Article
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
- Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen,Ecology and evolution · 2026Article
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Article
- Genomic perspectives on the inference of evolutionary changes in ecological life-history traits.Quantitative plant biology · 2026Review
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders.Scientific reports · 2025Article
- Biodiversity Genomics Research Practices Require Harmonising to Meet Stakeholder Needs in Conservation.Molecular ecology · 2025Review
- Machine learning-based discovery of informative SNPs for population assignment through whole genome sequencing.BMC genomics · 2025Article
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data.Heredity · 2025Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.
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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.