International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 90-99 http://medscipublisher.com/index.php/ijmms 90 Review and Progress Open Access The Application and Challenges of Integrating Multiomics Data in Individualized Therapy AnitaWang Physicov Med. Tech. Ltd., Zhejiang, Zhuji, 311800, Zhejiang, China Corresponding email: 2741098603@qq.com International Journal of Molecular Medical Science, 2024, Vol.14, No.1 doi: 10.5376/ijmms.2024.14.0012 Received: 08 May., 2024 Accepted: 10 Apr., 2024 Published: 22 Apr., 2024 Copyright © 2024 Wang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang A., 2024, The application and challenges of integrating multiomics data in individualized therapy, International Journal of Molecular Medical Science, 14(1): 90-99 (doi: 10.5376/ijmms.2024.14.0012) Abstract Personalized therapy is a treatment strategy tailored to individual patients based on their genetic, environmental, and lifestyle characteristics. With the rapid advancement of high-throughput sequencing technologies and other multi-omics techniques, a vast amount of multi-omics data can be used to guide personalized therapy decisions. However, the integration and analysis of multi-omics data present numerous challenges. This study discusses the applications and challenges of multi-omics data integration in personalized therapy, focusing on the sources and types of multi-omics data, methods and tools for multi-omics data integration, and their applications and challenges. The aim is to promote the application of multi-omics data in personalized medical practice and to further the development of personalized therapy. Keywords Personalized therapy; Multi-omics data; Integration; Application; Challenges Personalized therapy is a treatment strategy tailored to individual patients based on their genetic, phenotypic, and environmental characteristics. Compared to traditional disease treatment, personalized therapy is more precise and effective, significantly improving patient treatment response and survival rates. However, achieving personalized therapy requires an accurate understanding of the patient's disease characteristics and corresponding biomarkers. In recent years, with the rapid development and substantial cost reduction of high-throughput sequencing technologies, we have entered an era of big data in bioinformatics. Technologies such as genomics, transcriptomics, proteomics, and metabolomics generate vast amounts of data, providing unprecedented opportunities for disease research and treatment (Kang et al., 2022). Against this transformative backdrop, personalized therapy has become a highly focused area. The concept and methods of multi-omics data integration have become crucial tools for personalized therapy. Multi-omics data integration involves combining and analyzing multiple omics data sources from different levels to reveal the complex mechanisms of diseases and the specific responses of individuals. Comprehensive analysis not only uncovers deeper biological insights but also offers better personalized treatment plans for patients. However, multi-omics data integration in personalized therapy still faces numerous challenges. Different types of multi-omics data have differences in data structure, scale, and accuracy, making effective integration and interpretation a key issue. Data quality and consistency are also challenges (Canzler et al., 2020), as data may be affected by sample preparation, measurement errors, and data missing. Additionally, data privacy and ethical issues must be addressed. Therefore, this study introduces the applications and challenges of multi-omics data integration in personalized therapy. It discusses the sources and types of multi-omics data, introduces currently used data integration methods and tools, and explores the application fields of multi-omics data in personalized therapy. Furthermore, we will focus on the challenges and limitations of multi-omics data integration and propose future prospects and directions to promote the further development of personalized therapy, providing better medical services and efficacy for patients.
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