Legume Genomics and Genetics 2025, Vol.16, No.2, 91-99 http://cropscipublisher.com/index.php/lgg 97 Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Abdollahi-Arpanahi R., Gianola D., and Peñagaricano F., 2020, Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes, Genetics, Selection, Evolution, 52: 12. https://doi.org/10.1186/s12711-020-00531-z Azodi C., Bolger E., Mccarren A., Roantree M., De Los Campos G., and Shiu S., 2019, Benchmarking parametric and machine learning models for genomic prediction of complex traits, G3: Genes|Genomes|Genetics, 9: 3691-3702. https://doi.org/10.1534/g3.119.400498 Burgueño J., Crossa J., Cotes J., Vicente F., and Das B., 2011, Prediction assessment of linear mixed models for multienvironment trials, Crop Science, 51: 944-954. https://doi.org/10.2135/CROPSCI2010.07.0403 Chakraborty D., Elhegazy H., Elzarka H., and Gutierrez L., 2020, A novel construction cost prediction model using hybrid natural and light gradient boosting, Advanced Engineering Informatics, 46: 101201. https://doi.org/10.1016/j.aei.2020.101201 Chandra R., and Goyal S., 2021, Evaluation of deep learning models for multi-step ahead time series prediction, IEEE Access, 9: 83105-83123. https://doi.org/10.1109/ACCESS.2021.3085085 Conard A., DenAdel A., and Crawford L., 2023, A spectrum of explainable and interpretable machine learning approaches for genomic studies, Wiley Interdisciplinary Reviews: Computational Statistics, 15(5): e1617. https://doi.org/10.1002/wics.1617 Diers B., Specht J., Rainey K., Cregan P., Song Q., Ramasubramanian V., Graef G., Nelson R., Schapaugh W., Wang D., Shannon G., McHale L., Kantartzi S., Xavier A., Mian R., Stupar R., Michno J., An Y., Goettel W., Ward R., Fox C., Lipka A., Hyten D., Cary T., and Beavis W., 2018, Genetic architecture of soybean yield and agronomic traits, G3: Genes|Genomes|Genetics, 8: 3367-3375. https://doi.org/10.1534/g3.118.200332 Dong Q., Cheng Y., Li Y., Tong Y., Liu D., Yu J., Zhao N., Liu B., Ding X., and Xu C., 2025, Genome-wide association study and genomic prediction of essential agronomic traits in diversity panel of soybean varieties, Agronomy, 15(5): 1181. https://doi.org/10.3390/agronomy15051181 Doszhanova B., Zatybekov A., Didorenko S., Fang C., Abugalieva S., and Turuspekov Y., 2024, Genome-wide association study of seed quality and yield traits in a soybean collection from Southeast Kazakhstan, Agronomy, 14(11): 2746. https://doi.org/10.3390/agronomy14112746 Duan Z., Li Q., Wang H., He X., and Zhang M., 2023, Genetic regulatory networks of soybean seed size, oil and protein contents, Frontiers in Plant Science, 14: 1160418. https://doi.org/10.3389/fpls.2023.1160418 Fang C., Ma Y., Wu S., Liu Z., Wang Z., Yang R., Hu G., Zhou Z., Yu H., Zhang M., Pan Y., Zhou G., Ren H., Du W., Yan H., Wang Y., Han D., Shen Y., Liu S., Liu T., Zhang J., Qin H., Yuan J., Yuan X., Kong F., Liu B., Li J., Zhang Z., Wang G., Zhu B., and Tian Z., 2017, Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean, Genome Biology, 18: 161. https://doi.org/10.1186/s13059-017-1289-9 Fernandes I., Vieira C., Dias K., and Fernandes S., 2024, Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials, Theoretical and Applied Genetics, 137: 189. https://doi.org/10.1007/s00122-024-04687-w Gao W., Ma R., Li X., Liu J., Jiang A., Tan P., Xiong G., Du C., Zhang J., Zhang X., Fang X., Yi Z., and Zhang J., 2024, Construction of genetic map and QTL mapping for seed size and quality traits in soybean (Glycine max L.), International Journal of Molecular Sciences, 25(5): 2857. https://doi.org/10.3390/ijms25052857 Gill M., Anderson R., Hu H., Bennamoun M., Petereit J., Valliyodan B., Nguyen H., Batley J., Bayer P., and Edwards D., 2022, Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction, BMC Plant Biology, 22: 180. https://doi.org/10.1186/s12870-022-03559-z Goettel W., Zhang H., Li Y., Qiao Z., Jiang H., Hou D., Song Q., Pantalone V., Song B., Yu D., and An Y., 2022, POWR1 is a domestication gene pleiotropically regulating seed quality and yield in soybean, Nature Communications, 13: 3051. https://doi.org/10.1038/s41467-022-30314-7 Guo B., Sun L., Jiang S., Ren H., Sun R., Wei Z., Hong H., Luan X., Wang J., Wang X., Xu D., Li W., Guo C., and Qiu L., 2022, Soybean genetic resources contributing to sustainable protein production, Theoretical and Applied Genetics, 135: 4095-4121. https://doi.org/10.1007/s00122-022-04222-9 Hu Y., Liu Y., Wei J., Zhang W., Chen S., and Zhang J., 2023, Regulation of seed traits in soybean, aBIOTECH, 4: 372-385. https://doi.org/10.1007/s42994-023-00122-8
RkJQdWJsaXNoZXIy MjQ4ODYzNA==