Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
Ortiz Rios, Rodomiro Octavio
With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its effi-ciency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We out-line the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine var-ious statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.
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Эту запись предоставил Swedish University of Agricultural Sciences