# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 1 Introduction  
The field of omics research has been revolutionized by the rapid advancement of powerful and efficient technologies, enabling the generation of huge and complex datasets in various areas of omics, including genomics, epigenomics, proteomics, transcriptomics, and metabolomics. These molecular biology data provide unprecedented opportunities to uncover the complicated biological mechanisms underlying health, disease, and response to treatment [1]. Unlike other types of biological data (such as imaging or clinical phenotype data), molecular biology data directly reflect the cellular and molecular processes driving these mechanisms. By capturing the molecular makeup of biological systems--whether through genetic sequences, protein abundance, or metabolite concentrations--omics datasets provide deeper insights into the functional state of cells and tissues. This allows researchers to move beyond descriptive phenotypes to understanding the mechanistic basis of diseases, pinpointing the molecular drivers of health conditions and responses to treatments [2; 3]. However, the huge volume and heterogeneity of omics data have introduced significant challenges, particularly in the context of data integration. To fully realize the potential of these datasets, it has become necessary to develop computational platforms capable of integrating multiple omics, which allows a more complete view of biological systems. In recent years, the field of omics has experienced an exponential increase in the amount of data generated, driven by technological advancements in high-throughput sequencing and mass spectrometry techniques. A major contributor to this surge is the development of single-cell sequencing technologies, which allow the study of individual cells at unprecedented resolution [4]. These advances have not only provided more detailed insights into cellular heterogeneity but also expanded the complexity of data, requiring more sophisticated tools for integration and analysis. With the ability to profile thousands of cells across multiple omics layers in a single experiment, the demand for platforms that can efficiently handle this data explosion has intensified [5]. Multi-omics data integration refers to the process of combining datasets from different levels of omics (such as genomics and proteomics) to reveal new biological insights that are not possible through single-omics analysis [6]. While individual omics approaches, such as transcriptomics or proteomics, can provide valuable information, they are often insufficient on their own. For instance, transcriptomics can reveal gene expression patterns, but this data alone does not reflect post-transcriptional modifications or protein activity, which are critical for understanding functional outcomes in cells [7]. Multi-omics approaches address this limitation by allowing researchers to explore how different omics layers interact and contribute to a systemic view of biological processes. By capturing these complex interactions across multiple layers, multi-omics approaches provide a more robust, comprehensive understanding of disease mechanisms, enabling the identification of biomarkers that would not be evident from a single-omics perspective [8]. This innovative approach is particularly valuable in areas such as personalized medicine, clinical decision-making, and disease prediction, where the complex interactions between genes, proteins, metabolites, and epigenetic modifications play a critical role in health outcomes. Molecular biology data, in particular, offer the granularity and specificity needed to identify these complex interactions. Unlike environmental or lifestyle data, molecular data directly represent the cellular activities and molecular modifications driving health and disease [9; 10]. This makes them invaluable for revealing biomarkers and understanding pathways involved in disease onset, progression, and therapeutic response.