{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:02:55Z","timestamp":1776844975401,"version":"3.51.2"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Human Frontier Science Project","award":["RGY0075\/2019"],"award-info":[{"award-number":["RGY0075\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and\/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>dnadna is a Python (\u22653.7) package, its repository is available at gitlab.com\/mlgenetics\/dnadna and its associated documentation at mlgenetics.gitlab.io\/dnadna\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac765","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T15:08:34Z","timestamp":1669648114000},"source":"Crossref","is-referenced-by-count":10,"title":["<tt>dnadna<\/tt>: a deep learning framework for population genetics inference"],"prefix":"10.1093","volume":"39","author":[{"given":"Th\u00e9ophile","family":"Sanchez","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik Madison","family":"Bray","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Jobic","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"},{"name":"ENS Paris-Saclay, 91190 Gif-sur-Yvette , France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00e9r\u00e9my","family":"Guez","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"},{"name":"UMR7206 Eco-Anthropologie, Mus\u00e9um National d\u2019Histoire Naturelle, CNRS, Universit\u00e9 de Paris , 75016 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne-Catherine","family":"Letournel","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Charpiat","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6462-8783","authenticated-orcid":false,"given":"Jean","family":"Cury","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"},{"name":"SEED, U1284, INSERM, Universit\u00e9 de Paris , 75004 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-4730","authenticated-orcid":false,"given":"Flora","family":"Jay","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Num\u00e9rique , 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"2023010805363505100_btac765-B1","doi-asserted-by":"crossref","first-page":"e54967","DOI":"10.7554\/eLife.54967","article-title":"A community-maintained standard library of population genetic models","volume":"9","author":"Adrion","year":"2020","journal-title":"eLife"},{"key":"2023010805363505100_btac765-B2","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1093\/molbev\/msaa038","article-title":"Predicting the landscape of recombination using deep learning","volume":"37","author":"Adrion","year":"2020","journal-title":"Mol. 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