Fig. 1.

Schematic overview of the MaLAdapt workflow. To train MaLAdapt, we simulate 1000 randomly sampled genomic segments of 5MB length with a realistic genic structure, recombination rate, and distribution of deleterious mutations under modern human demography with archaic adaptive introgression (AI). We extract summary statistics in sliding 50kb-windows as features and train a hierarchical decision tree algorithm (ETC) with data labeled with binary AI and non-AI classes. After comprehensive model optimization, testing, and feature selection (supplementary figs. S4–S5, Supplementary Material online), we apply the trained model to empirical modern human genomic data to predict AI candidates.