Open AccessDeep‐learning power and perspectives for genomic selection
Author(s) -
MontesinosLópez Osval Antonio,
MontesinosLópez Abelardo,
HernandezSuarez Carlos Moises,
BarrónLópez José Alberto,
Crossa José
Publication year - 2021
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.1002/tpg2.20122
Subject(s) - artificial intelligence , selection (genetic algorithm) , machine learning , exploit , task (project management) , computer science , deep learning , engineering , systems engineering , computer security
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.