# SHADES

Telling neutrality, selective sweeps, balancing selection and adaptive introgression apart is hard because the haplotype footprints these processes leave overlap. SHADES renders haplotype frequencies, differences between frequency distributions, and sequence differences between ranked haplotypes as three image types, trains convolutional networks at window sizes of 11, 21 and 51 SNPs, and stacks the three scales for four-class classification. On simulated test replicates, the deeper SHADES[M2] reached 90.85% overall accuracy, against 67.83% for SURFDAWave, 68.30% for diploS/HIC, 65.20% for TrIdent, 63.20% for FAST-NN and 79.43% for FASTER-NN on the same test set.

Telling neutrality, selective sweeps, balancing selection and adaptive introgression apart is hard because the haplotype footprints these processes leave overlap. SHADES renders haplotype frequencies, differences between frequency distributions, and sequence differences between ranked haplotypes as three image types, trains convolutional networks at window sizes of 11, 21 and 51 SNPs, and stacks the three scales for four-class classification. On simulated test replicates, the deeper SHADES[M2] reached 90.85% overall accuracy, against 67.83% for SURFDAWave, 68.30% for diploS/HIC, 65.20% for TrIdent, 63.20% for FAST-NN and 79.43% for FASTER-NN on the same test set.