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Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here, we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    ,\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>2<\/jats:sub>\n                    ),\n                    <jats:italic>P<\/jats:italic>\n                    <jats:sub>3<\/jats:sub>\n                    ), Out) and a matrix of pairwise nucleotide divergence (\n                    <jats:italic>d<\/jats:italic>\n                    <jats:sub>XY<\/jats:sub>\n                    ) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe\u2010CNN, was able to accurately perform model selection for hybridization scenarios across a wide breath of parameter space. We then used HyDe\u2010CNN to test models of admixture in\n                    <jats:italic>Heliconius<\/jats:italic>\n                    butterflies, as well as comparing it to phylogeny\u2010based introgression statistics. Given the flexibility of our approach, the dropping cost of long\u2010read sequencing and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.\n                  <\/jats:p>","DOI":"10.1111\/1755-0998.13355","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T04:24:46Z","timestamp":1615177486000},"page":"2676-2688","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Chromosome\u2010scale inference of hybrid speciation and admixture with convolutional neural networks"],"prefix":"10.1111","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9177-8958","authenticated-orcid":false,"given":"Paul D.","family":"Blischak","sequence":"first","affiliation":[{"name":"Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ USA"},{"name":"Department of Molecular and Cellular Biology University of Arizona Tucson AZ USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7173-1319","authenticated-orcid":false,"given":"Michael S.","family":"Barker","sequence":"additional","affiliation":[{"name":"Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan N.","family":"Gutenkunst","sequence":"additional","affiliation":[{"name":"Department of Molecular and Cellular Biology University of Arizona Tucson AZ USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"Abadi M. 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D.(2020).Predicting geographic location from genetic1variation with deep neural networks.bioRxiv https:\/\/doi.org\/10.1101\/2019.12.11.872051","DOI":"10.7554\/eLife.54507.sa2"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-ecolsys-102209-144621"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/162.4.2025"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","unstructured":"Blischak P. D. Barker M. S. &Gutenkunst R. 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