ReviewNature reviews. Genetics2024
Harnessing deep learning for population genetic inference.
Review in Nature reviews. Genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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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
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Genomic insights into natural selection in recent human history.Nature reviews. Genetics · 2026Review
- Neural posterior estimation for population genetics.Genetics · 2026Article
- Integrative analyses elucidate transcriptional regulatory functions of risk alleles for metabolic liver disease.Nature genetics · 2026Article
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- 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
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
- Harnessing Deep Learning in Searching Wild Relatives of Domestic Animals.Molecular ecology resources · 2026Article
- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Quantum inspired feature engineering for explainable EEG signal classification.Scientific reports · 2026Article
- Genomic insights into the survival code of karst plants.Plant diversity · 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
- Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integrated genetic and geographic ancestry prediction via large-scale genomic data and machine learning.Human genomics · 2025Article
- Integrative functional genomics reveals transcriptional regulatory function of risk alleles for metabolic liver disease.Research square · 2025Article
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- Lineage-specific regulatory evolution: insights from massively parallel reporter assays.Current opinion in genetics & development · 2025Review
- Advances in molecular pathology and therapy of non-small cell lung cancer.Signal transduction and targeted therapy · 2025Review
- Potential and pitfalls of using identity-by-descent for malaria genomic surveillance.Trends in parasitology · 2025Review
- ConfuseNN: Interpreting convolutional neural network inferences in population genomics with data shuffling.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.
Indexed as
Identifiers
37666948What 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.