ArticlebioRxiv : the preprint server for biology2026
Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Detecting signatures of positive selection in genomes is a common application of population genetics and one of the most influential models for this task is the hard selective sweep where a de novo mutation rapidly fixes. Many statistics have been developed to detect hard sweeps, often attempting to summarize signatures left behind in the site frequency, spectrum, linkage disequilibrium, and haplotype frequency. However, potentially undiscovered signals could still exist. We attempted to test whether any undiscovered signatures of hard sweeps exist by comparing machine learning models, which can learn signatures from raw data without any prior knowledge, to known summary statistics for inferring the time to fixation
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