ArticleBMC bioinformatics2023
disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.
Article in BMC bioinformatics, 2023. 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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10 citing papers in PubMed.
- On the use of generative models for demographic inference in malaria vectors from genomic data.G3 (Bethesda, Md.) · 2026Article
- Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 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
- Accessible, Realistic Genome Simulation with Selection Using stdpopsim.Molecular biology and evolution · 2025Article
- Accessible, realistic genome simulation with selection using stdpopsim.bioRxiv : the preprint server for biology · 2025Article
- Sweeps in Space: Leveraging Geographic Data to Identify Beneficial Alleles in Anopheles gambiae.Molecular biology and evolution · 2025Article
- Patterns of Gene Flow inEvolutionary applications · 2024Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
- Tree sequences as a general-purpose tool for population genetic inference.bioRxiv : the preprint server for biology · 2024Article
- Estimation of spatial demographic maps from polymorphism data using a neural network.Molecular ecology resources · 2024Article
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Abstract
Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses the geographic information that comes with each sample. These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively. disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input. The software is open source and available from https://github.com/kr-colab/disperseNN2 , with documentation located at https://dispersenn2.readthedocs.io/en/latest/ .
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