Multi-Omics Integration
Combining multiple biological data layers to understand disease and discover drugs
Multi-omics integration is the computational combination of multiple biological data types—genomics, transcriptomics, proteomics, and metabolomics—to understand complex biological systems, identify disease mechanisms, and discover therapeutic targets.
The Omics Layers
Why Multi-Omics Beats Single-Omics
Single-omics provides only a partial view of biology. Multi-omics captures the complete flow of information from genome to phenotype:
mRNA levels correlate poorly with protein abundance due to translation regulation and protein stability.
Biological pathways interact in complex ways that only become visible when integrating multiple data types.
Cells compensate for perturbations at different biological levels—genomic changes may be buffered at protein level.
Multi-omics enables causal inference: does a mutation cause expression changes that drive protein and metabolite dysregulation?
Applications in Drug Discovery
Target Identification
Find disease-driving targets by tracing molecular changes across genomic, transcriptomic, and proteomic layers.
Biomarker Discovery
Identify multi-modal biomarker signatures that are more robust than single-analyte markers.
Patient Stratification
Classify patients into molecular subtypes based on integrated omics profiles for precision medicine.
Mechanism of Action
Understand drug effects by tracking changes across all biological layers after treatment.
Frequently Asked Questions
What is multi-omics integration?
Multi-omics integration is the computational combination of genomics, transcriptomics, proteomics, and metabolomics data to understand complex biological systems and discover therapeutic targets.
What methods are used for multi-omics integration?
Common methods include: network-based integration (building molecular interaction networks), machine learning approaches (deep learning, matrix factorization), pathway analysis (mapping data to known biological pathways), and Bayesian methods for probabilistic integration.