Applications of multi-omics analysis in human diseases

Area of Science:

• Biomedical Research
• Computational Biology
• Genomics

Background:

• Multi-omics, integrating genomics, transcriptomics, proteomics, and metabolomics, is crucial for advancing human disease studies.
• Existing reviews often focus on technology development or specific diseases, lacking a holistic view of multi-omics.

Purpose of the Study:

• To provide a systematic and comprehensive introduction to multi-omics technologies and their applications.
• To detail integrated analysis methods, particularly machine learning and deep learning approaches.
• To guide researchers in the emerging field of multi-omics medical research.

Main Methods:

• Exploration of multi-omics technical categories and experimental design considerations.
• Focus on integrated analysis methods, including machine learning and deep learning algorithms.
• Review of applications in cancer, neurodegenerative diseases, aging, and drug discovery.

Main Results:

• Outlines diverse multi-omics technical categories and their integration strategies.
• Highlights the pivotal role of machine learning and deep learning in analyzing complex multi-omics data.
• Showcases applications across various medical fields and identifies relevant open-source tools and databases.

Conclusions:

• Multi-omics integration, especially single-cell and spatial multi-omics, is vital for future disease research and precision medicine.
• Addresses current challenges and future directions in multi-omics data analysis and application.
• Provides essential guidance for researchers entering the multi-omics field.