Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

ABSTRACT: Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art

SUBMITTER: Mo Z 

PROVIDER: S-EPMC10655966 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature