Emerging Trends in Systems Biology: Multi-Omics Integration and Beyond
Ning Wang, Guocheng Zhang, Manman Li
Abstract
This study analyzes the framework and key technologies of multi-omics integration, including the combination of genomics, transcriptomics, proteomics, metabolomics, and epigenomics. It also discusses the computational tools and data analysis methods used in multi-omics integration, such as network construction, machine learning, and big data visualization, which are essential for processing and interpreting multi-omics data. With the rapid advancement of multi-omics technologies, data integration offers a holistic view of biological systems, enabling a deeper understanding of complex biological processes. Through case studies in fields such as personalized medicine and agriculture, this study demonstrates the practical applications of these integrative approaches, highlighting the importance of multi-omics in advancing personalized medicine, agriculture, and environmental research. Additionally, it aims to address the technical challenges in multi-omics data integration and provide insights into future directions, including real-time integration and the application of artificial intelligence.