Computational Molecular Biology 2025, Vol.15, No.5, 235-244 http://bioscipublisher.com/index.php/cmb 242 platform should not be too academic. It's better to be accessible to everyone for promotion, especially when collaborating across breeding units. Furthermore, the sample size still needs to be expanded, and multiple varieties should be studied simultaneously; otherwise, the conclusion will always be one-sided. Finally, artificial intelligence and the Internet of Things have entered the field. After the real-time cloud upload of breeding data, the efficiency of feedback and modeling has also improved to a new level. In the future, making dynamic adjustment breeding decisions will no longer be a dream. Multi-omics is not the end; it is merely a means. By integrating these "visible" technologies with "practical" breeding practices, future dairy cow breeding will not only focus on science but also on efficiency and sustainability. Acknowledgments We would like to express our gratitude to the reviewers for their valuable feedback, which helped improve the manuscript. 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 Amin A., Zaman W., and Park S., 2025, Harnessing multi-omics and predictive modeling for climate-resilient crop breeding: from genomes to fields, Genes, 16(7): 809. https://doi.org/10.3390/genes16070809 Bekele R., Taye M., Abebe G., and Meseret S., 2023, Genomic regions and candidate genes associated with milk production traits in holstein and its crossbred cattle: a review, International Journal of Genomics, 2023: 8497453. https://doi.org/10.1155/2023/8497453 Brito L., Bédère N., Douhard F., Oliveira H., Arnal M., Peñagaricano F., Schinckel A., Baes C., and Miglior F., 2021, Review: Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world, Animal, 15(Suppl 1): 100292. https://doi.org/10.1016/j.animal.2021.100292 Buaban S., Lengnudum K., Boonkum W., and Phakdeedindan P., 2021, Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model, Journal of Dairy Science, 105(1): 468-494. https://doi.org/10.3168/jds.2020-19826 Cai W., Cole J., Goddard M., Li J., Zhang S., and Song J., 2025, Mammary gland multi-omics data reveals new genetic insights into milk production traits in dairy cattle, PLOS Genetics, 21(4): e1011675. https://doi.org/10.1371/journal.pgen.1011675 Cortes-Hernández J., García-Ruiz A., Peñagaricano F., Montaldo H., and Ruiz-Lopez F., 2025, Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows, PLOS ONE, 20(2): e0314888. https://doi.org/10.1371/journal.pone.0314888 Dong W., Yang J., Zhang Y., Liu S., Ning C., Ding X., Wang W., Zhang Y., Zhang Q., and Jiang L., 2021, Integrative analysis of genome-wide DNA methylation and gene expression profiles reveals important epigenetic genes related to milk production traits in dairy cattle, Journal of Animal Breeding and Genetics, 138(5): 562-573. https://doi.org/10.1111/jbg.12530 Fotso-Kenmogne P., Carneiro P., Silva D., Cobuci J., Aponte P., Oliveira H., and Brito L., 2025, Genomic-based genetic parameters for daily milk yield and various lactation persistency traits in American Holstein cattle, Journal of Dairy Science, 108(7): 7329-7344. https://doi.org/10.3168/jds.2024-25836 Frizzarin M., Gormley I., Berry D., Murphy T., Casa A., Lynch A., and McParland S., 2021, Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods, Journal of Dairy Science, 104(7): 7438-7447. https://doi.org/10.3168/jds.2020-19576 Gao Y., Liu G., Ma L., Fang L., Li C., and Baldwin R., 2024, Transcriptomic profiling of gastrointestinal tracts in dairy cattle during lactation reveals molecular adaptations for milk synthesis, Journal of Advanced Research, 71: 67-80. https://doi.org/10.1016/j.jare.2024.06.020 Gong L., Lou Q., Yu C., Chen Y., Hong J., Wu W., Fan S., Chen L., and Liu C., 2022, GpemDB: a scalable database architecture with the multi-omics entity-relationship model to integrate heterogeneous big-data for precise crop breeding, Frontiers in Bioscience, 27(5): 159. https://doi.org/10.31083/j.fbl2705159 Han B., Lin S., Ye W., Chen A., Liu Y., and Sun D., 2025, COL6A1 promotes milk production and fat synthesis through the PI3K-Akt/insulin/AMPK/PPAR signaling pathways in dairy cattle, International Journal of Molecular Sciences, 26(5): 2255. https://doi.org/10.3390/ijms26052255 Jin L., Xu L., Jin H., Zhao S., Jia Y., Li J., and Hua J., 2024, Accuracy of genomic predictions cross populations with different linkage disequilibrium patterns, Genes, 15(11): 1419. https://doi.org/10.3390/genes15111419
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