TY  - JA
AU  - Crossa,J.
AU  - Martini,J.W.R.
AU  - Vitale,P.
AU  - Perez-Rodriguez,P.
AU  - Costa-Neto,G.
AU  - Fritsche-Neto,R.
AU  - Runcie,D.E.
AU  - Cuevas,J.
AU  - Toledo,F.H.
AU  - Huihui Li
AU  - De Vita,P.
AU  - Gerard,G.S.
AU  - Dreisigacker,S.
AU  - Crespo-Herrera,L.A.
AU  - Saint Pierre,C.
AU  - Bentley,A.R.
AU  - Lillemo,M.
AU  - Ortiz,R.
AU  - Montesinos-Lopez,O.A.
AU  - Montesinos-Lopez,A.
TI  - Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
SN  - 1360-1385
PY  - 2025///
CY  - United States of America
PB  - Elsevier Ltd.
KW  - Models
KW  - AGROVOC
KW  - Plant breeding
KW  - Genomics
KW  - Forecasting
KW  - Software development
KW  - Marker-assisted selection
KW  - Statistical methods
KW  - Machine learning
N1  - Peer review; Open access
N2  - With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various 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
UR  - https://hdl.handle.net/10883/35536
T2  - Trends in Plant Science
DO  - https://doi.org/10.1016/j.tplants.2024.12.009
ER  -