ReviewTrends in genetics : TIG2018
Supervised Machine Learning for Population Genetics: A New Paradigm.
Review in Trends in genetics : TIG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 214 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
214 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Genomic Selection for Milk Yield and Milk Composition Traits in Dairy Goats Using Machine Learning and Prior-Information Models.Animals : an open access journal from MDPI · 2026Article
- ARGformer: learning on ancestral recombination graphs with transformers.Bioinformatics (Oxford, England) · 2026Article
- Neural posterior estimation for population genetics.Genetics · 2026Article
- On the use of generative models for demographic inference in malaria vectors from genomic data.G3 (Bethesda, Md.) · 2026Article
- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- A Rarefaction Approach to Identify Local Introgression in a Three Population Tree.bioRxiv : the preprint server for biology · 2026Article
- Leveraging Genetic Diversity for Abiotic Stress Tolerance.International journal of molecular sciences · 2026Article
- Genetic basis of temporal variation in phenology in a woody perennial plant.NPJ systems biology and applications · 2026Article
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Article
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Discriminating models of trait evolution.Evolution; international journal of organic evolution · 2026Article
- Harnessing Deep Learning in Searching Wild Relatives of Domestic Animals.Molecular ecology resources · 2026Article
- AI solutions for evolutionary genomics of nonmodel species.Evolution letters · 2026Review
- Neural posterior estimation for population genetics.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
- Article
154 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.
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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.