Interpreting generative adversarial networks to infer natural selection from genetic data.

ABSTRACT: Understanding natural selection and other forms of non-neutrality is a major focus for the use of machine learning in population genetics. Existing methods rely on computationally intensive simulated training data. Unlike efficient neutral coalescent simulations for demographic inference, realistic simulations of selection typically require slow forward simulations. Because there are many possible modes of selection, a high dimensional parameter space must be explored, with no guarantee that the simulated models are close to the real processes. Finally, it is difficult to interpret trained neural networks, leading to a lack of understanding about what features contribute to classification. Here we develop a new approach to detect selection and other local evolutionary processes that requir

SUBMITTER: Riley R 

PROVIDER: S-EPMC10990424 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature