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Genomics enters the deep learning era
The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235815/ https://www.ncbi.nlm.nih.gov/pubmed/35769139 http://dx.doi.org/10.7717/peerj.13613 |
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author | Routhier, Etienne Mozziconacci, Julien |
author_facet | Routhier, Etienne Mozziconacci, Julien |
author_sort | Routhier, Etienne |
collection | PubMed |
description | The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences. |
format | Online Article Text |
id | pubmed-9235815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92358152022-06-28 Genomics enters the deep learning era Routhier, Etienne Mozziconacci, Julien PeerJ Bioinformatics The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences. PeerJ Inc. 2022-06-24 /pmc/articles/PMC9235815/ /pubmed/35769139 http://dx.doi.org/10.7717/peerj.13613 Text en © 2022 Routhier and Mozziconacci https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Routhier, Etienne Mozziconacci, Julien Genomics enters the deep learning era |
title | Genomics enters the deep learning era |
title_full | Genomics enters the deep learning era |
title_fullStr | Genomics enters the deep learning era |
title_full_unstemmed | Genomics enters the deep learning era |
title_short | Genomics enters the deep learning era |
title_sort | genomics enters the deep learning era |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235815/ https://www.ncbi.nlm.nih.gov/pubmed/35769139 http://dx.doi.org/10.7717/peerj.13613 |
work_keys_str_mv | AT routhieretienne genomicsentersthedeeplearningera AT mozziconaccijulien genomicsentersthedeeplearningera |