International Journal of Molecular Ecology and Conservation, 2026, Vol.16, No.1, 31-43 http://ecoevopublisher.com/index.php/ijmec 42 researchers can continuously record livestock growth, health, and behavioral patterns. Through cloud databases and data-mining algorithms, precise dynamic phenotypic models can be built to enable individual tracking and group performance prediction. This digital monitoring approach not only improves the objectivity and timeliness of data collection but also supports large-scale breeding and health management with intelligent tools. Artificial intelligence (AI) and machine learning algorithms will play an increasingly important role in genetic and phenotypic evaluation. AI can automatically identify key factors affecting genetic stability and trait expression from large-scale omics and phenotypic datasets, constructing predictive models. For example, deep neural networks can perform pattern recognition on phenotypic outcomes associated with different editing sites, assisting in selecting optimal insertion sites and gene constructs. In the future, AI will be deeply integrated with bioinformatics platforms to form a Smart Breeding System, enabling integrated decision support for gene design, phenotypic evaluation, environmental monitoring, and risk prediction. Overall, future research on transgenic livestock will move toward precision, safety, intelligence, and sustainability. Through the coordinated advancement of technological innovation, ethical governance, and intelligent evaluation, transgenic livestock are expected to make forward-looking contributions to global food security, medical health, and agricultural modernization, while ensuring animal welfare and ecological safety. References Bertolini L.R., Meade H., Lazzarotto C.R., Martins L.T., Tavares K.C., Bertolini M., and Murray J.D., 2016, The transgenic animal platform for biopharmaceutical production, Transgenic Research, 25(3): 329-343. Eriksson S., Jonas E., Rydhmer L., and Röcklinsberg H., 2018, Invited review: breeding and ethical perspectives on genetically modified and genome-edited cattle, Journal of Dairy Science, 101(1): 1-17. https://doi.org/10.3168/jds.2017-12962 Evangelou A., Ignatiou A., Antoniou C., Kalanidou S., Chatzimatthaiou S., Shianiou G., Ellina S., Athanasiou R., Panagi M., Apidianakis Y., and Pitsouli C., 2018, Unpredictable effects of the genetic background of transgenic lines in physiological quantitative traits, G3: Genes|Genomes|Genetics, 9: 3877-3890. https://doi.org/10.1534/g3.119.400715 Hryhorowicz M., Lipiński D., Hryhorowicz S., Nowak-Terpiłowska A., Ryczek N., and Zeyland J., 2020, Application of genetically engineered pigs in biomedical research, Genes, 11(6): 670. https://doi.org/10.3390/genes11060670 König S., and May K., 2019, Invited review: phenotypic strategies and quantitative-genetic background of resistance, tolerance, and resilience-associated traits in dairy cattle, Animal, 13(5): 897-908. Laible G., Wei J., and Wagner S., 2015, Improving livestock for agriculture – technological progress from random transgenesis to precision genome editing heralds a new era, Biotechnology Journal, 10(1): 109-120. https://doi.org/10.1002/biot.201400193 Niemann H., and Kues W.A., 2003, Application of transgenesis in livestock for agriculture and biomedicine, Animal Reproduction Science, 79(3-4): 291-317. Park T., 2023, Gene-editing techniques and their applications in livestock and beyond, Animal Bioscience, 36(2): 333-338. https://doi.org/10.5713/ab.22.0383 Pinkert C.A., 2014, Transgenic animal technology: a laboratory handbook, Newnes, Oxford, pp. 1-691. Pursel V., Pinkert C., Miller K., Bolt D., Campbell R., Palmiter R., Brinster R., and Hammer R., 1989, Genetic engineering of livestock, Science, 244(4910): 1281-1288. https://doi.org/10.1126/science.2499927 Robl J.M., Wang Z., Kasinathan P., and Kuroiwa Y., 2007, Transgenic animal production and animal biotechnology, Theriogenology, 67(1): 127-133. Shaukat M., 2021, Review on transgenic technology in livestock: current status and future horizons, Pakistan Journal of Science, 73(1): 1. https://doi.org/10.57041/pjs.v73i1.657 Van Cott K., Luboń H., Russell C., Butler S., Gwazdauskas F., Knight J., Drohan W., and Velander W., 1997, Phenotypic and genotypic stability of multiple lines of transgenic pigs expressing recombinant human protein C, Transgenic Research, 6(3): 203-212. https://doi.org/10.1023/a:1018442124584 Wang S., Qu Z., Huang Q., Zhang J., Lin S., Yang Y., Meng F., Li J., and Zhang K., 2022, Application of gene editing technology in resistance breeding of livestock, Life, 12(7): 1070. https://doi.org/10.3390/life12071070 Wheeler M.B., and Walters E.M., 2001, Transgenic technology and applications in swine, Theriogenology, 56(8): 1345-1369. https://doi.org/10.1016/s0093-691x(01)00635-5
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