# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 1 Introduction
By integrating multiple layers of omics, a better understanding of the underlying molecular mechanisms of disease, the identification of potential biomarkers for early detection, and the ability to tailor treatments can be achieved more effectively. In recent years, various digital platforms have been developed to facilitate the integration and analysis of multi-omics data [11]. Platforms such as GraphOmics and OmicsAnalyst are specifically designed to address the complexity of multi-omics datasets. GraphOmics, for instance, excels in network-based visualizations, enabling researchers to explore molecular interactions and pathways through interactive clustering and enrichment analysis. Similarly, OmicsAnalyst focuses on user accessibility, offering an intuitive web-based interface that combines multi-omics integration with machine learning tools for predictive modeling and biomarker discovery. These tools not only handle data visualization but also cater to users with varying levels of computational expertise, enhancing accessibility and usability for a broader audience. By providing user-friendly interfaces,they allow access to complex data analysis for clinicians, biologists, and researchers who may not have extensive computational expertise [12]. This accessibility speeds up the process of data interpretation and discovery, empowering healthcare professionals and biological researchers to make data-driven decisions more quickly. Furthermore, platforms democratize multi-omics analysis, making it available to a wider audience, not just specialists with high technical skills. This ensures that discoveries and analyses are not limited to a select group, promoting inclusivity in the scientific and healthcare communities. These platforms allow the exploration of the relationships between different types of omics, providing deeper insights into disease pathways and molecular networks. Moreover, they hold great promise in clinical applications, from diagnosis to personalized treatment strategies, supporting healthcare professionals in making more evidence-based decisions through integrated data analysis. Despite these advances, significant challenges remain in the area of multi-omics integration [13]. Issues related to data normalization, interoperability between platforms, and the handling of large, complex datasets continue to constrain progress. In addition, the increasing dependency on machine learning (ML)-based analytics and artificial intelligence (AI) in healthcare underscores the need for platforms that can seamlessly integrate these advanced computational techniques. As the demand for more powerful and user-friendly tools grows, there is an urgent need for further development of platforms that provide both advanced analytics and give priority to security and accessibility for users with different levels of technical expertise. This review presents a comprehensive analysis of such digital platforms, exploring their capabilities to address the complexities of multi-omics data. In this review, we evaluate how these platforms facilitate the integration of multi-omics data to reveal innovative biological insights, while offering ease of use to a broad range of researchers. Additionally, the review will highlight how these platforms contribute to improved clinical decision-making and personalized medicine by enabling more accurate and data-driven analysis. Our work examines the strengths and limitations of tools mentioned in these studies, particularly in the areas of data visualization, scalability, and accessibility for non-expert users. By summarizing the main features and functionalities of these platforms, we aim to provide a clear overview of their role in current omics research and healthcare. Furthermore, the review will examine the major challenges facing these platforms, including the need for standardization, improved ML/AI-based analysis, and enhanced utility.