International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 90-99 http://medscipublisher.com/index.php/ijmms 94 3 Applications of Multi-omics Data Integration in Personalized Therapy 3.1 Disease prediction and diagnosis One of the important applications of multi-omics data integration in personalized therapy is in disease prediction and diagnosis. By integrating data from various biomarkers, such as genomics, transcriptomics, proteomics, and metabolomics, a more comprehensive understanding of disease mechanisms can be achieved (Figure 2), providing more accurate information for personalized disease prediction and diagnosis. Figure 2 Conceptual model of the association between multi-omics and human diseases (Adopted from blog.sciencenet.cn) By integrating multi-omics data, disease-related biomarkers can be identified, and predictive models can be developed to assess disease risk. For example, genomic data can be used to discover genetic variants associated with disease risk, transcriptomic data can reveal gene expression patterns, and proteomic data can identify protein biomarkers related to diseases. By combining these data, classifiers or risk assessment models can be constructed to help doctors determine a patient's likelihood of developing a disease and take preventive or early treatment measures. Multi-omics data integration can provide more comprehensive and detailed disease diagnosis information. By measuring the genomics, transcriptomics, proteomics, etc., of patient samples and comparing them with known disease patterns in databases, the type and characteristics of the disease can be identified. For example, analyzing mutations, copy number variations, and chromosomal rearrangements in genomic data can classify and grade tumor types (Menyhárt and Győrffy, 2021). Additionally, combining transcriptomic and proteomic data can further define the molecular phenotype and drug sensitivity of the disease, providing a basis for precision treatment. 3.2 Drug development and screening Another important application of multi-omics data integration in personalized therapy is in drug development and screening. By integrating biological data from different levels, the molecular mechanisms of diseases can be better understood, new drug targets can be discovered, and drug efficacy and side effects can be predicted, thereby accelerating the drug development and personalized drug screening process (Li et al., 2020). Integrating data from genomics, transcriptomics, and proteomics can reveal key molecules and pathways involved in disease development. These data can be used to identify new drug targets, which are critical protein molecules involved in disease mechanisms. Further research on these potential targets can lead to the design of appropriate drug molecules to target diseases.
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