Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.05.237834v1?rss=1 Authors: Wang, Z., Wang, J., Kourakos, M., Hoang, N., Lee, H. H., Mathieson, I., Mathieson, S. Abstract: Population genetics relies heavily on simulated data for validation, inference, and intuition. In particular, since real data is always limited, simulated data is crucial for training machine learning methods. Simulation software can accurately model evolutionary processes, but requires many hand-selected input parameters. As a result, simulated data often fails to mirror the properties of real genetic data, which limits the scope of methods that rely on it. In this work, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method is based on a generative adversarial network that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation-with-migration model. We then apply our method to human data from the 1000 Genomes Project, and show that we can accurately recapitulate the features of real data. Copy rights belong to original authors. Visit the link for more info