Computational Molecular Biology 2024, Vol.14, No.2, 64-75 http://bioscipublisher.com/index.php/cmb 68 4 Applications of Multi-Omics Data Integration 4.1 Precision medicine Multi-omics data integration has revolutionized the field of precision medicine by enabling the development of personalized treatment strategies. By combining data from genomics, transcriptomics, proteomics, and metabolomics, researchers can gain a comprehensive understanding of the molecular mechanisms underlying individual diseases. This holistic approach allows for the identification of patient-specific therapeutic targets and the design of tailored treatment regimens. For instance, in oncology, multi-omics analyses have facilitated the stratification of patients based on molecular subtypes, leading to more effective and personalized cancer therapies (Nicora et al., 2020; Kang et al., 2021; Raufaste-Cazavieille et al., 2022). Additionally, the integration of multi-omics data with clinical information has been shown to improve the prediction of treatment responses and disease outcomes, further enhancing the precision of medical interventions (Reel et al., 2021; Terranova and Venkatakrishnan, 2023). The discovery of reliable biomarkers is crucial for the early diagnosis, prognosis, and monitoring of diseases. Multi-omics data integration has emerged as a powerful tool for biomarker discovery, as it allows for the simultaneous analysis of multiple molecular layers. This approach can reveal complex interactions and regulatory networks that are not apparent when examining single-omics data alone. For example, in cancer research, multi-omics analyses have identified novel biomarkers that can predict disease progression and response to therapy, thereby guiding clinical decision-making (Olivier et al., 2019; Turanli et al., 2019; Demirel et al., 2021). Moreover, the integration of multi-omics data has led to the identification of biomarkers for various other diseases, including autoimmune disorders and cardiovascular diseases, highlighting its broad applicability in precision medicine (Subramanian et al., 2020; Reel et al., 2021). Predictive modeling is a key component of precision medicine, as it enables the anticipation of disease trajectories and treatment outcomes. The integration of multi-omics data with advanced machine learning algorithms has significantly enhanced the accuracy and robustness of predictive models. These models can capture the complex, nonlinear relationships between different molecular entities and clinical variables, providing valuable insights into disease mechanisms and therapeutic responses (Kang et al., 2021; Cominetti et al., 2023; Terranova and Venkatakrishnan, 2023). For instance, in oncology, predictive models based on multi-omics data have been used to forecast patient survival, treatment efficacy, and potential adverse effects, thereby informing personalized treatment plans (Nicora et al., 2020; Raufaste-Cazavieille et al., 2022). Additionally, the use of multi-omics data in predictive modeling has been extended to other therapeutic areas, such as neurology and immunology, demonstrating its versatility and potential to transform clinical practice (Reel et al., 2021; Terranova and Venkatakrishnan, 2023). 4.2 Drug discovery and development The integration of multi-omics data has also made significant contributions to drug discovery and development. By providing a comprehensive view of the molecular landscape of diseases, multi-omics analyses can identify novel drug targets and elucidate the mechanisms of action of existing drugs. This information is invaluable for the development of new therapeutic agents and the repurposing of existing drugs for new indications (Turanli et al., 2019; Demirel et al., 2021). For example, network-based approaches that integrate multi-omics data have been used to identify key regulatory nodes in cancer and other diseases, leading to the discovery of potential drug targets (Turanli et al., 2019; Subramanian et al., 2020). Furthermore, multi-omics data integration can facilitate the optimization of drug dosing and the identification of biomarkers for patient stratification in clinical trials, thereby improving the efficiency and success rate of drug development (Kang et al., 2021; Terranova and Venkatakrishnan, 2023). 4.3 Understanding complex diseases Complex diseases, such as cancer, cardiovascular diseases, and neurodegenerative disorders, are characterized by intricate molecular interactions and regulatory networks. Multi-omics data integration provides a powerful approach to unravel these complexities by combining information from different molecular layers. This holistic
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