# multiomics

> study of multiple omics

**Wikidata**: [Q25110691](https://www.wikidata.org/wiki/Q25110691)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Multiomics)  
**Source**: https://4ort.xyz/entity/multiomics

## Summary
Multiomics is the study of multiple omics, integrating data from different biological domains such as genomics, proteomics, and metabolomics to provide a comprehensive understanding of biological systems. It is a subfield of computational biology that uses data-analytical and theoretical methods to analyze complex biological data.

## Key Facts
- Multiomics is classified under computational biology and omics in knowledge bases
- It has a UMLS CUI identifier: C5690853
- The term "multi-omics" is an alias for multiomics
- Multiomics has Wikipedia articles in 6 languages: English, Spanish, French, Korean, Ukrainian, and Chinese
- It is associated with the Medical Subject Headings (MeSH) descriptor ID: D000095028
- Multiomics is linked to Google Knowledge Graph ID: /g/11bwfm8jsy
- It has 6 sitelinks across Wikimedia projects
- Single-cell multiomics is a specialized application studying multiple omics at the single-cell level

## FAQs
### Q: What is multiomics?
A: Multiomics is the integrated study of multiple biological "omics" domains, such as genomics, proteomics, and metabolomics, to understand complex biological systems through computational analysis.

### Q: How is multiomics different from single omics studies?
A: Multiomics combines data from multiple biological domains simultaneously, providing a more comprehensive view of biological systems compared to single omics studies that focus on one domain at a time.

### Q: What fields use multiomics?
A: Multiomics is primarily used in computational biology, systems biology, and biomedical research to study complex biological systems and diseases.

## Why It Matters
Multiomics represents a paradigm shift in biological research by enabling scientists to analyze multiple layers of biological information simultaneously. This integrated approach provides a more complete understanding of biological systems than studying individual omics domains in isolation. By combining genomics, proteomics, metabolomics, and other omics data, researchers can uncover complex interactions and regulatory mechanisms that would be missed when examining single data types. This comprehensive view is particularly valuable for understanding complex diseases, developing personalized medicine approaches, and advancing our fundamental knowledge of biological processes. Multiomics has become essential for modern systems biology and is driving innovations in drug discovery, disease diagnosis, and therapeutic development.

## Notable For
- Integration of multiple omics data types for comprehensive biological analysis
- Application in single-cell analysis through single-cell multiomics
- Classification as a specialized branch of computational biology
- Presence in major biomedical knowledge bases with standardized identifiers
- Support for multiple languages in Wikipedia, indicating global research interest

## Body
### Classification and Relationships
Multiomics is formally classified as a subclass of both computational biology and omics. This dual classification reflects its methodological approach (computational analysis) and its subject matter (biological omics data). The field is closely related to systems biology, as both aim to understand complex biological interactions, though multiomics specifically focuses on integrating multiple omics data types.

### Technical Applications
Single-cell multiomics represents a specialized application of multiomics technology, enabling researchers to analyze multiple omics layers from individual cells. This approach is particularly valuable for understanding cellular heterogeneity and rare cell populations in tissues. The technology has applications in cancer research, developmental biology, and immunology.

### Knowledge Representation
Multiomics is well-represented in biomedical knowledge bases, with standardized identifiers including UMLS CUI C5690853 and MeSH descriptor ID D000095028. These identifiers facilitate data integration and retrieval across different biomedical databases and research platforms. The presence of Wikipedia articles in six languages demonstrates the field's international research community and global relevance.

### Data Integration Methods
The field employs various computational methods for integrating heterogeneous omics data, including statistical analysis, machine learning algorithms, and network-based approaches. These methods must handle different data types, scales, and measurement units while identifying meaningful biological patterns and relationships across the integrated datasets.

## References

1. UMLS 2023