Deep‐learning power and perspectives for genomic selection

Abstract Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but...

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Autores principales:	Osval Antonio Montesinos‐López, Abelardo Montesinos‐López, Carlos Moises Hernandez‐Suarez, José Alberto Barrón‐López, José Crossa
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/38fa095794c74453ac08f20bcf87606a
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spelling	oai:doaj.org-article:38fa095794c74453ac08f20bcf87606a2021-12-05T07:50:11ZDeep‐learning power and perspectives for genomic selection1940-337210.1002/tpg2.20122https://doaj.org/article/38fa095794c74453ac08f20bcf87606a2021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20122https://doaj.org/toc/1940-3372Abstract Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but nowadays, artificial machines have surpassed the ability of humans in this specific task. This is just one example of how the application of these models has surpassed human abilities and the performance of other machine‐learning algorithms. For this reason, DL models have been adopted for genomic selection (GS). In this article we provide insight about the power of DL in solving complex prediction tasks and how combining GS and DL models can accelerate the revolution provoked by GS methodology in plant breeding. Furthermore, we will mention some trends of DL methods, emphasizing some areas of opportunity to really exploit the DL methodology in GS; however, we are aware that considerable research is required to be able not only to use the existing DL in conjunction with GS, but to adapt and develop DL methods that take the peculiarities of breeding inputs and GS into consideration.Osval Antonio Montesinos‐LópezAbelardo Montesinos‐LópezCarlos Moises Hernandez‐SuarezJosé Alberto Barrón‐LópezJosé CrossaWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Osval Antonio Montesinos‐López Abelardo Montesinos‐López Carlos Moises Hernandez‐Suarez José Alberto Barrón‐López José Crossa Deep‐learning power and perspectives for genomic selection
description	Abstract Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but nowadays, artificial machines have surpassed the ability of humans in this specific task. This is just one example of how the application of these models has surpassed human abilities and the performance of other machine‐learning algorithms. For this reason, DL models have been adopted for genomic selection (GS). In this article we provide insight about the power of DL in solving complex prediction tasks and how combining GS and DL models can accelerate the revolution provoked by GS methodology in plant breeding. Furthermore, we will mention some trends of DL methods, emphasizing some areas of opportunity to really exploit the DL methodology in GS; however, we are aware that considerable research is required to be able not only to use the existing DL in conjunction with GS, but to adapt and develop DL methods that take the peculiarities of breeding inputs and GS into consideration.
format	article
author	Osval Antonio Montesinos‐López Abelardo Montesinos‐López Carlos Moises Hernandez‐Suarez José Alberto Barrón‐López José Crossa
author_facet	Osval Antonio Montesinos‐López Abelardo Montesinos‐López Carlos Moises Hernandez‐Suarez José Alberto Barrón‐López José Crossa
author_sort	Osval Antonio Montesinos‐López
title	Deep‐learning power and perspectives for genomic selection
title_short	Deep‐learning power and perspectives for genomic selection
title_full	Deep‐learning power and perspectives for genomic selection
title_fullStr	Deep‐learning power and perspectives for genomic selection
title_full_unstemmed	Deep‐learning power and perspectives for genomic selection
title_sort	deep‐learning power and perspectives for genomic selection
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/38fa095794c74453ac08f20bcf87606a
work_keys_str_mv	AT osvalantoniomontesinoslopez deeplearningpowerandperspectivesforgenomicselection AT abelardomontesinoslopez deeplearningpowerandperspectivesforgenomicselection AT carlosmoiseshernandezsuarez deeplearningpowerandperspectivesforgenomicselection AT josealbertobarronlopez deeplearningpowerandperspectivesforgenomicselection AT josecrossa deeplearningpowerandperspectivesforgenomicselection
_version_	1718372548643651584

Deep‐learning power and perspectives for genomic selection

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