@article {Kern267229, author = {Andrew D. Kern and Daniel R. Schrider}, title = {diploS/HIC: an updated approach to classifying selective sweeps}, elocation-id = {267229}, year = {2018}, doi = {10.1101/267229}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes}, URL = {https://www.biorxiv.org/content/early/2018/03/22/267229}, eprint = {https://www.biorxiv.org/content/early/2018/03/22/267229.full.pdf}, journal = {bioRxiv} }