ArticleMolecular ecology resources2021
Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks.
Article in Molecular ecology resources, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.bioRxiv : the preprint server for biology · 2026Article
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- ConfuseNN: Interpreting convolutional neural network inferences in population genomics with data shuffling.bioRxiv : the preprint server for biology · 2025Article
- Harnessing deep learning for population genetic inference.Nature reviews. Genetics · 2024Review
- Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes.Molecular biology and evolution · 2023Article
- Deep Learning in Population Genetics.Genome biology and evolution · 2023Article
- Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks.Molecular ecology resources · 2021Article
- Detecting adaptive introgression in human evolution using convolutional neural networks.eLife · 2021Article
- Reference-free discovery of nuclear SNPs permits accurate, sensitive identification ofApplications in plant sciencesArticle
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Authors and funding
3 authors.
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Abstract
Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here, we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P
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