GAISHI: A Python Package for Detecting Ghost Introgression with Machine Learning
Journal:
bioRxiv
Published Date:
Feb 3, 2026
Abstract
Summary: Ghost introgression is a challenging problem in population genetics. Recent studies have explored supervised learning models, namely logistic regression and UNet++, to detect genomic footprints of ghost introgression. However, their applicability is limited because existing implementations are tailored to tasks in their respective publications, but not available as software implementations. Here, we present GAISHI, a Python package for identifying introgressed segments and alleles using machine learning and demonstrate its usage in a Human-Neanderthal introgression scenario. Availabity and implementation: GAISHI is available on GitHub under the GNU General Public License v3.0. The source code can be found at https://github.com/xin-huang/gaishi.
