TY  - JA
AU  - Montesinos-Lopez,O.A.
AU  - Crespo-Herrera,L.A.
AU  - Saint Pierre,C.
AU  - Cano-Paez,B.
AU  - Huerta Prado,G.I.
AU  - Mosqueda-Gonzalez,B.A.
AU  - Ramos-Pulido,S.
AU  - Gerard,G.S.
AU  - Khalid Alnowibet
AU  - Fritsche-Neto,R.
AU  - Montesinos-Lopez,A.
AU  - Crossa,J.
TI  - Feature engineering of environmental covariates improves plant genomic-enabled prediction
SN  - 1664-462X
PY  - 2024///
CY  - Switzerland
PB  - Frontiers Media S.A., 
KW  - Engineering
KW  - AGROVOC
KW  - Selection
KW  - Marker-assisted selection
KW  - Plant breeding
N1  - Peer review; Open Access
N2  - Introduction: Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods: When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion: We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates
UR  - https://hdl.handle.net/10883/34616
T2  - Frontiers in Plant Science
DO  - https://doi.org/10.3389/fpls.2024.1349569
ER  -