INTERPRETING CONVOLUTIONAL NEURAL NETWORKS IN POPULATION GENETICS.

ABSTRACT: Machine learning approaches have become a powerful alternative to traditional methods in population genetics. Convolutional neural networks (CNNs) in particular have been successful in inferring natural selection, recombination rate estimation, introgression, dispersal distances, and effective population size changes. One limitation of CNNs and other deep learning methods is that they can be difficult to interpret. When they have been shown to be as or more successful than summary-statistic-based methods, what are they learning? Here we investigate CNNs from two different methods: the pg-gan discriminator for identifying real vs. simulated data, and two networks trained to detect selective sweeps. We first compute correlations between learned network features and traditional summary

SUBMITTER: Xu H 

PROVIDER: S-EPMC12710823 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature