Crop genomic selection with deep learning and environmental data: A survey.

Autor:	Jubair S; Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada., Domaratzki M; Department of Computer Science, University of Western Ontario, London, ON, Canada.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2023 Jan 10; Vol. 5, pp. 1040295. Date of Electronic Publication: 2023 Jan 10 (Print Publication: 2022).
DOI:	10.3389/frai.2022.1040295
Abstrakt:	Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Jubair and Domaratzki.)
Databáze:	MEDLINE
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