MPB_2024v15n1

Molecular Plant Breeding 2024, Vol.15, No.1, 15-26 http://genbreedpublisher.com/index.php/mpb 25 van Dijk A.D.J., Kootstra G., Kruijer W., and de Ridder D., 2021, Machine learning in plant science and plant breeding, Iscience, 24(1): 1-12. https://doi.org/10.1016/j.isci.2020.101890 PMid:33364579 PMCid:PMC7750553 Veillet F., Perrot L., Chauvin L., Kermarrec M., Guyon-Debast A., Chauvin J., Nogué F., and Mazier M., 2019, Transgene-free genome editing in tomato and potato plants using agrobacterium-mediated delivery of a CRISPR/Cas9 cytidine base editor, International Journal of Molecular Sciences, 20(2): 402. https://doi.org/10.3390/ijms20020402 PMid:30669298 PMCid:PMC6358797 Wallace J.G., Rodgers-Melnick E., and Buckler E.S., 2018, On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics, Annu. Rev. Genet., 52: 421-444. https://doi.org/10.1146/annurev-genet-120116-024846 PMid:30285496 Wang Z., Jiang Y., Liu Z., Tang X., and Li H., 2022, Machine learning and ensemble learning for transcriptome data: principles and advances, 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), IEEE, pp.676-683. https://doi.org/10.1109/AEMCSE55572.2022.00137 Weiß T.M., Zhu X., Leiser W.L., Li D., Liu W., Schipprack W., Melchinger A.E., Hahn V., Würschum T., 2022, Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zeamays L.), G3(Bethesda), 12(3): jkab445. https://doi.org/10.1093/g3journal/jkab445 PMid:35100379 PMCid:PMC8895988 Whelan A., and Lema M., 2017, A research program for the socioeconomic impacts of gene editing regulation, GM Crops & Food, 8(1): 74-83. https://doi.org/10.1080/21645698.2016.1271856 PMid:28080208 PMCid:PMC5592976 Wilson F., Flint H., Deaton W., and Buehler R., 1994, Yield, yield components, and fiber properties of insect‐resistant cotton lines containing a bacillus thuringiensis toxin gene, Crop Science, 34: 38-41. https://doi.org/10.2135/cropsci1994.0011183X003400010006x Wu S., Wen W., Gou W., Lu X., Zhang W., Zheng C., Xiang Z., Chen L., and Guo X., 2022, A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction, Frontiers in Plant Science, 13: 897746. https://doi.org/10.3389/fpls.2022.897746 PMid:36003825 PMCid:PMC9393617 Xing Y., Lv P., He H., Leng J., Yu H., and Feng X., 2022, Traits expansion and storage of soybean phenotypic data in computer vision-based test, Frontiers in Plant Science, 13: 832592. https://doi.org/10.3389/fpls.2022.832592 PMid:35300012 PMCid:PMC8921532 Xu J., Hua K., and Lang Z., Genome editing for horticultural crop improvement, Hortic. Res., 6: 113. https://doi.org/10.1038/s41438-019-0196-5 PMid:31645967 PMCid:PMC6804600 Xu R., Li H., Qin R., Li J., Qiu C., Yang Y., Ma H., Li L., Wei P., and Yang J., 2015, Generation of inheritable and “transgene clean” targeted genome-modified rice in later generations using the CRISPR/Cas9 system, Scientific Reports, 5(1): 11491. https://doi.org/10.1038/srep11491 PMid:26089199 PMCid:PMC5155577 Yan J., and Wang X.F., 2022, Machine learning bridges omics sciences and plant breeding, Treads in plant Science, 28(2): 199-210. https://doi.org/10.1016/j.tplants.2022.08.018 PMid:36153276 Yasumoto S., Sawai S., Lee H., Mizutani M., Saito K., Umemoto N., and Muranaka T., 2020, Targeted genome editing in tetraploid potato through transient TALEN expression by agrobacterium infection, Plant Biotechnology, 37(2): 205-211. https://doi.org/10.5511/plantbiotechnology.20.0525a PMid:32821228 PMCid:PMC7434673 Zhang F., Wen Y., and Guo X., 2014, CRISPR/Cas9 for genome editing: progress, implications and challenges, Human Molecular Genetics, 23(R1): R40-R46. https://doi.org/10.1093/hmg/ddu125

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