Jie  Zhang

Institute of Life Sciences, Jiyang Colloge of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 2   
Received: 09 Feb., 2024    Accepted: 20 Mar., 2024    Published: 07 Apr., 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 analyzed the latest trends, challenges, and future directions of multi omics data integration. High throughput technology enables the generation of large amounts of data at multiple omics levels, including genomics, transcriptomics, proteomics, and metabolomics. However, integrating these heterogeneous datasets faces significant challenges due to differences in data types, dimensions, and a lack of standardized analysis protocols. We discussed various integration methods, including data-driven, knowledge driven, and machine learning approaches, with a focus on their applications in disease subtype classification, biomarker discovery, and precision medicine. In addition, we also analyzed the computational and visualization challenges associated with single-cell multi omics data and proposed future directions for developing stronger and more interpretable integration strategies, hoping to provide a comprehensive overview of the current status of multi omics data integration and demonstrate its potential in translational biomedical research and clinical practice.

Multi-omics integration; High-throughput technologies; Machine learning; Precision medicine; Single-cell analysis