Computational Molecular Biology 2024, Vol.14, No.5, 211-219 http://bioscipublisher.com/index.php/cmb 213 In oncology, machine learning methodologies have been crucial for patient phenotyping, biomarker discovery, and drug repurposing, demonstrating the potential of multi-omics data to drive precision medicine (Nicora et al., 2020). 3.3 Big data processing and visualization The integration of multi-omics data generates large, multidimensional datasets that require advanced computational tools for processing and visualization. High-throughput omic approaches can produce tera- to peta-byte sized data files, posing significant challenges in data cleaning, normalization, and storage (Misra et al., 2019). Effective data integration strategies, such as early, mixed, intermediate, late, and hierarchical integration, are essential for managing these large datasets and extracting meaningful insights. Visualization tools and portals are also critical for interpreting multi-omics data, allowing researchers to explore complex biological processes and interactions. The development of standardized analytical pipelines and visualization frameworks is necessary to handle the increasing volume and complexity of multi-omics data, ultimately leading to a holistic understanding of biological systems (Joshi et al., 2020). 4 Applications of Multi-Omics Integration in Personalized Medicine 4.1 Multi-omics in cancer personalized treatment 4.1.1 Integrating genomic and transcriptomic data for cancer therapy The integration of genomic and transcriptomic data has significantly advanced the field of cancer therapy by enabling a deeper understanding of tumor biology and heterogeneity. By combining these data types, researchers can identify driver mutations and their downstream effects on gene expression, which is crucial for developing targeted therapies. For instance, multi-omics approaches have been used to classify tumors more accurately, predict patient prognosis, and identify novel therapeutic targets (Figure 1) (Zhao et al., 2020; Menyhárt and Győrffy, 2021; Vlachavas et al., 2021). These integrated analyses help in understanding the complex molecular mechanisms underlying cancer, thereby facilitating the development of personalized treatment strategies. 4.1.2 Role of proteomics in identifying cancer biomarkers Proteomics plays a pivotal role in the identification of cancer biomarkers, which are essential for early diagnosis, prognosis, and treatment monitoring. The integration of proteomic data with other omics layers, such as genomics and transcriptomics, enhances the sensitivity and specificity of biomarker discovery. Recent studies have shown that proteomics can reveal protein expression patterns and post-translational modifications that are not detectable at the genomic or transcriptomic levels (Hristova and Chan, 2019; Ivanisevic and Sewduth, 2023). This multi-omics approach has led to the identification of several potential biomarkers that could be translated into clinical practice, although challenges remain in their validation and implementation. 4.1.3 Metabolomics for understanding cancer metabolism Metabolomics provides insights into the metabolic alterations associated with cancer, which are critical for understanding tumor biology and developing metabolic-targeted therapies. By integrating metabolomic data with genomic, transcriptomic, and proteomic information, researchers can map out the metabolic pathways that are dysregulated in cancer. This comprehensive view helps in identifying metabolic vulnerabilities that can be targeted for therapy. For example, metabolomics has been used to study the Warburg effect and other metabolic reprogramming events in cancer cells, offering new avenues for therapeutic intervention (Raufaste-Cazavieille et al., 2022). 4.2 Cardiovascular disease risk assessment Multi-omics integration is also being applied to assess the risk of cardiovascular diseases (CVD). By combining genomic, transcriptomic, proteomic, and metabolomic data, researchers can identify biomarkers and molecular pathways associated with CVD risk. This holistic approach allows for a more accurate prediction of disease risk and the development of personalized prevention and treatment strategies. Studies have shown that multi-omics data can reveal novel biomarkers that are not detectable by single-omics approaches, thereby improving the precision of CVD risk assessment (Olivier et al., 2019).
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