{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:55:02Z","timestamp":1768996502381,"version":"3.49.0"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004795","name":"Institut Universitaire de France","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004795","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Prediction of genomic annotations from DNA sequences using deep learning is today becoming a flourishing field with many applications. Nevertheless, there are still difficulties in handling data in order to conveniently build and train models dedicated for specific end-user\u2019s tasks. keras_dna is designed for an easy implementation of Keras models (TensorFlow high level API) for genomics. It can handle standard bioinformatic files formats as inputs such as bigwig, gff, bed, wig, bedGraph or fasta and returns standardized inputs for model training. keras_dna is designed to implement existing models but also to facilitate the development of news models that can have single or multiple targets or inputs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Freely available with a MIT License using pip install keras_dna or cloning the github repo at https:\/\/github.com\/etirouthier\/keras_dna.git.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa929","type":"journal-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T15:31:07Z","timestamp":1603121467000},"page":"1593-1594","source":"Crossref","is-referenced-by-count":8,"title":["keras_dna: a wrapper for fast implementation of deep learning models in genomics"],"prefix":"10.1093","volume":"37","author":[{"given":"Etienne","family":"Routhier","sequence":"first","affiliation":[{"name":"Sorbonne Universite, CNRS, Laboratoire de Physique Th\u00e9orique de la Mati\u00e8re Condens\u00e9e (LPTMC) , Paris F-75252, France"}]},{"given":"Ayman","family":"Bin Kamruddin","sequence":"additional","affiliation":[{"name":"Sorbonne Universite, CNRS, Laboratoire de Physique Th\u00e9orique de la Mati\u00e8re Condens\u00e9e (LPTMC) , Paris F-75252, France"},{"name":"Mus\u00e9um National d\u2019Histoire Naturelle, Structure et Instabilit\u00e9 des G\u00e9nomes, UMR7196 , Paris 75231, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5652-0302","authenticated-orcid":false,"given":"Julien","family":"Mozziconacci","sequence":"additional","affiliation":[{"name":"Sorbonne Universite, CNRS, Laboratoire de Physique Th\u00e9orique de la Mati\u00e8re Condens\u00e9e (LPTMC) , Paris F-75252, France"},{"name":"Mus\u00e9um National d\u2019Histoire Naturelle, Structure et Instabilit\u00e9 des G\u00e9nomes, UMR7196 , Paris 75231, France"}]}],"member":"286","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"key":"2023051716260350400_btaa929-B1","author":"Abadi","year":"2015"},{"key":"2023051716260350400_btaa929-B2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. 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