Computational Molecular Biology 2024, Vol.14, No.2, 64-75 http://bioscipublisher.com/index.php/cmb 69 perspective can reveal novel insights into disease etiology, progression, and heterogeneity, which are essential for the development of effective therapeutic strategies (Olivier et al., 2019; Nicora et al., 2020; Raufaste-Cazavieille et al., 2022). For instance, in cancer research, multi-omics analyses have uncovered the molecular diversity within tumors, leading to the identification of distinct molecular subtypes and the development of targeted therapies (Demirel et al., 2021; Raufaste-Cazavieille et al., 2022). Similarly, in neurodegenerative diseases, multi-omics data integration has shed light on the molecular mechanisms underlying disease onset and progression, paving the way for the development of novel diagnostic and therapeutic approaches (Reel et al., 2021; Terranova and Venkatakrishnan, 2023).The integration of multi-omics data is transforming our understanding of complex diseases and driving the advancement of precision medicine. 5 Challenges in Multi-Omics Data Integration 5.1 Data heterogeneity One of the primary challenges in multi-omics data integration is the inherent heterogeneity of the data. Different omics layers, such as genomics, transcriptomics, proteomics, and metabolomics, each have unique characteristics, data formats, and scales, making their integration complex. For instance, genomic data is often discrete and categorical, while proteomic and metabolomic data are typically continuous and quantitative. This disparity necessitates sophisticated normalization and transformation techniques to harmonize the data before integration (Misra et al., 2019; Subramanian et al., 2020; Kaur et al., 2021). Moreover, the nomenclature and identifiers used across different omics datasets can vary significantly, complicating the process of matching corresponding entities across datasets. For example, gene identifiers in genomic data may not directly correspond to protein identifiers in proteomic data, requiring extensive cross-referencing and mapping efforts (Misra et al., 2019). The heterogeneity also extends to the experimental designs and conditions under which the data are collected, adding another layer of complexity to the integration process (Bodein et al., 2020). 5.2 Computational complexity The integration of multi-omics data is computationally intensive due to the high dimensionality and large volume of the datasets involved. High-throughput technologies generate vast amounts of data, often in the tera- to peta-byte range, which poses significant challenges for data storage, processing, and analysis (Misra et al., 2019; Lee et al., 2020). The computational burden is further exacerbated by the need to perform complex operations such as data cleaning, normalization, and dimensionality reduction before meaningful integration can occur. Advanced computational methods, including machine learning and deep learning techniques, have been developed to address these challenges. However, these methods themselves are computationally demanding and require substantial computational resources and expertise to implement effectively (Nicora et al., 2020; Benkirane et al., 2023). For instance, deep learning models, while powerful, necessitate extensive training on large datasets, which can be time-consuming and resource-intensive (Benkirane et al., 2023). Additionally, the integration process often involves constructing and analyzing complex network models to represent the relationships between different omics layers. These network models, such as heterogeneous multi-layered networks (HMLNs), are computationally challenging to build and analyze due to their complexity and the need to infer novel biological relations from the integrated data (Lee et al., 2020). 5.3 Data interpretation and biological relevance Even after successful integration, interpreting the integrated multi-omics data and deriving biologically relevant insights remain significant challenges. The complexity of the integrated data can obscure meaningful biological signals, making it difficult to draw clear conclusions about the underlying biological processes (Ebrahim et al., 2016; Subramanian et al., 2020). For example, while integrated data may reveal correlations between different omics layers, determining the causal relationships and biological significance of these correlations requires careful analysis and validation (Ebrahim et al., 2016). Furthermore, the interpretation of multi-omics data often relies on sophisticated visualization techniques to make the data comprehensible. However, current visualization methods
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