LGG_2025v16n2

Legume Genomics and Genetics 2025, Vol.16, No.2, 91-99 http://cropscipublisher.com/index.php/lgg 98 Li W., Boer M., Joosen R., Zheng C., Percival-Alwyn L., Cockram J., and Van Eeuwijk F., 2024, Modeling QTL-by-environment interactions for multi-parent populations, Frontiers in Plant Science, 15: 1410851. https://doi.org/10.3389/fpls.2024.1410851 Liu K., 1997, Chemistry and nutritional value of soybean components, Soybeans, 2: 25-113. https://doi.org/10.1007/978-1-4615-1763-4_2 Liu S., Liu Z., Hou X., and Li X., 2023, Genetic mapping and functional genomics of soybean seed protein, Molecular Breeding, 43: 29. https://doi.org/10.1007/s11032-023-01373-5 López O., González B., López A., and Crossa J., 2023, Statistical machine-learning methods for genomic prediction using the SKM library, Genes, 14(5): 1003. https://doi.org/10.3390/genes14051003 Lourenço V., Ogutu J., Rodrigues R., and Piepho H., 2022, Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data, BMC Genomics, 25: 152. https://doi.org/10.1101/2022.06.09.495423 Monaco A., Pantaleo E., Amoroso N., Lacalamita A., Lo Giudice C., Fonzino A., Fosso B., Picardi E., Tangaro S., Pesole G., and Bellotti R., 2021, A primer on machine learning techniques for genomic applications, Computational and Structural Biotechnology Journal, 19: 4345-4359. https://doi.org/10.1016/j.csbj.2021.07.021 Montesinos-López O., Montesinos-López A., Pérez-Rodríguez P., Barrón-López J., Martini J., Fajardo-Flores S., Gaytán-Lugo L., Santana-Mancilla P., and Crossa J., 2021, A review of deep learning applications for genomic selection, BMC Genomics, 22: 19. https://doi.org/10.1186/s12864-020-07319-x Mumford M., Forknall C., Rodriguez D., Eyre J., and Kelly A., 2023, Incorporating environmental covariates to explore genotype×environment×management (G×E×M) interactions: a one-stage predictive model, Field Crops Research, 304: 109133. https://doi.org/10.1016/j.fcr.2023.109133 Nguyen H., Vu T., Vo T., and Thai H., 2021, Efficient machine learning models for prediction of concrete strengths, Construction and Building Materials, 266(Part B): 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950 Norberg A., Abrego N., Blanchet F., Adler F., Anderson B., Anttila J., Araújo M., Dallas T., Dunson D., Elith J., Foster S., Fox R., Franklin J., Godsoe W., Guisan A., O’Hara, B., Hill N., Holt R., Hui F., Husby M., Kålås, J., Lehikoinen A., Luoto M., Mod H., Newell G., Renner I., Roslin T., Soininen J., Thuiller W., Vanhatalo J., Warton D., White M., Zimmermann N., Gravel D., and Ovaskainen O., 2019, A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels, Ecological Monographs, 89(3): e01370. https://doi.org/10.1002/ECM.1370 Parmley K., Higgins R., Ganapathysubramanian B., Sarkar S., and Singh A., 2019, Machine learning approach for prescriptive plant breeding, Scientific Reports, 9: 17132. https://doi.org/10.1038/s41598-019-53451-4 Patil G., Mian R., Vuong T., Pantalone V., Song Q., Chen P., Shannon G., Carter T., and Nguyen H., 2017, Molecular mapping and genomics of soybean seed protein: a review and perspective for the future, Theoretical and Applied Genetics, 130: 1975-1991. https://doi.org/10.1007/s00122-017-2955-8 Piepho H., and Williams E., 2024, Factor‐analytic variance-covariance structures for prediction into a target population of environments, Biometrical Journal, 66(6): e202400008. https://doi.org/10.1002/bimj.202400008 Rao Y., Zhang L., Gao L., Wang S., and Yang L., 2025, ExAutoGP: enhancing genomic prediction stability and interpretability with automated machine learning and SHAP, Animals, 15(8): 1172. https://doi.org/10.3390/ani15081172 Ray S., Jarquín D., and Howard R., 2022, Comparing artificial‐intelligence techniques with state‐of‐the‐art parametric prediction models for predicting soybean traits, The Plant Genome, 16(1): e20263. https://doi.org/10.1002/tpg2.20263 Robinson R., Palczewska A., Palczewski J., and Kidley N., 2017, Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets, Journal of Chemical Information and Modeling, 57(8): 1773-1792. https://doi.org/10.1021/acs.jcim.6b00753 Tayade R., Imran M., Ghimire A., Khan W., Nabi R., and Kim Y., 2023, Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean, Frontiers in Plant Science, 14: 1195210. https://doi.org/10.3389/fpls.2023.1195210 Van Der Laan L., Parmley K., Saadati M., Pacin H., Panthulugiri S., Sarkar S., Ganapathysubramanian B., Lorenz A., and Singh A., 2024, Genomic and phenomic prediction for soybean seed yield, protein, and oil, The Plant Genome, 18(1): e70002. https://doi.org/10.1002/tpg2.70002 Vymyslický T., Trněný O., Rietman H., Balko C., Đorđević V., Ranđelović P., and Dybová M., 2025, Phenotypic characterization of soybean genetic resources at multiple locations: breeding implications for enhancing environmental resilience, yield and protein content, Frontiers in Plant Science, 16: 1422162. https://doi.org/10.3389/fpls.2025.1422162

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