International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 90-99 http://medscipublisher.com/index.php/ijmms 98 integration can help identify new drug targets, predict drug responses, and optimize drug dosages, thereby enhancing drug efficacy and reducing adverse reactions. However, multi-omics data integration also faces challenges. The collection and analysis of different data types involve various technical platforms and methods, requiring solutions for data consistency, standardization, and integration. Multi-omics data are typically high-dimensional and large-scale, necessitating efficient computational methods and sufficient computational resources for processing and analysis. Additionally, new algorithms and models need to be developed to accommodate the requirements of different data types and integration scenarios. With further advancements in technology and optimization of data integration methods, the application prospects of multi-omics data integration in personalized therapy are vast. Continuous research and improvement in data integration methods and tools are needed to develop more accurate and efficient algorithms and to enhance data standardization and sharing norms. Furthermore, privacy protection and ethical issues must be adequately addressed to safeguard individual data privacy and uphold ethical principles. Multi-omics data integration plays a crucial role in personalized therapy, providing strong support for medical decision-making and drug development. Despite facing challenges, ongoing research and innovation will likely lead to more breakthroughs in multi-omics data integration, bringing new opportunities and hope for disease prevention, diagnosis, and treatment. Acknowledgements I would like to express my gratitude to Ms. Liu Chuchu for her assistance throughout the stages of topic selection, data collection, and article review. References Afgan E., Baker D., Batut B., Van Den Beek M., Bouvier D., Čech M., Chilton J., Clements D., Coraor N., Grüning B.A., Guerler A., Hillman-Jackson J., Hiltemann S., Jalili V., Rasche H., Soranzo N.,Goecks J.,Taylor J., Nekrutenko A., and Blankenberg D., 2018, The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update, Nucleic. Acids. Res., 46(W1): W537-W544. https://doi.org/10.1093/nar/gky379 PMid:29790989 PMCid:PMC6030816 Canzler S., Schor J., Busch W., Schubert K., Rolle-Kampczyk U.E., Seitz H., Kamp H., Bergen M.V., Buesen R., and Hackermüller, J., 2020, Prospects and challenges of multi-omics data integration in toxicology, Archives of Toxicology, 94: 371-388. https://doi.org/10.1007/s00204-020-02656-y PMid:32034435 Kang M., Ko E., and Mersha T.B., 2022, A roadmap for multi-omics data integration using deep learning, Briefings in Bioinformatics, 23(1): bbab454. https://doi.org/10.1093/bib/bbab454 PMid:34791014 PMCid:PMC8769688 Li K., Du Y., Li L., and Wei, D.Q., 2020, Bioinformatics approaches for anti-cancer drug discovery, Current drug targets, 21(1): 3-17. https://doi.org/10.2174/1389450120666190923162203 PMid:31549592 Liu J.F., Li W.L., Wang L, Li J., Li E.W., and Luo Y.M., 2022, Multi-omics technology and its applications to life sciences: a review, Shengwu Gongcheng Xuebao (Chinese Journal of Biotechnology), 38(10): 3581-3593. Liu X., Yang Y., Ge Y.P., and Lin T., 2021, Application of artificial intelligence in clinical genomics, Zhongguo Yixue Kexueyuan Xuebao (Acta Academiae Medicinae Sinicae). 43(6): 950-955. Manni M., Berkeley M.R., Seppey M., and Zdobnov E.M., 2021, BUSCO: assessing genomic data quality and beyond, Current Protocols, 1(12): e323. https://doi.org/10.1002/cpz1.323 PMid:34936221 Menyhárt O., and Győrffy B., 2021, Multi-omics approaches in cancer research with applications in tumor subtype, prognosis, and diagnosis. Computational and structural biotechnology journal, 19: 949-960. https://doi.org/10.1016/j.csbj.2021.01.009 PMid:33613862 PMCid:PMC7868685 Pang Y.J., Lyu J., Yu C.Q., Sun D.J.Y., and Li L.M., 2021, A multi-omics approach to investigate the etiology of non-communicable diseases: recent advance and applications, Zhonghua Liuxingbingxue Zazhi (Chinese Journal of Epidemiology), 42(1): 1-9.
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