Ning Wang , Guocheng Zhang , Manman Li

Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 5   
Received: 15 Aug., 2024    Accepted: 20 Sep., 2024    Published: 25 Oct., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

Systems biology; Multi-omics integration; Data visualization; Personalized medicine; Artificial intelligence (AI)