ArticleAdvances in neural information processing systems2018
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks.
Article in Advances in neural information processing systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed, 59 citations in OpenAlex.
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- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
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- Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026Article
- Unifying Summary Statistic Selection for Approximate Bayesian Computation.Statistics and computing · 2026Article
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- Signatures of soft selective sweeps predominate in the yellow fever mosquitobioRxiv : the preprint server for biology · 2025Article
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- Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning.Molecular biology and evolution · 2025Article
- ConfuseNN: Interpreting convolutional neural network inferences in population genomics with data shuffling.bioRxiv : the preprint server for biology · 2025Article
- Efficient detection and characterization of targets of natural selection using transfer learning.bioRxiv : the preprint server for biology · 2025Article
- Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN.Communications biology · 2025Article
- Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations.Genetics · 2025Article
- Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.Applied network science · 2025Article
- Constructing ancestral recombination graphs through reinforcement learning.Frontiers in genetics · 2025Article
- Digital Image Processing to Detect Adaptive Evolution.Molecular biology and evolution · 2024Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our framework can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.
Identifiers
33244210PMC7687905W2963306315What 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.