"/>Research area

Multi-Omics Integration

Fusing many layers into one biology — integrating genome, transcriptome, proteome and microbiome for systems-level insight.

0
omic layers
0
joint model
0
+ integration methods
Overview

What this area is.

No single assay tells the whole story. Multi-omics integration jointly models DNA, RNA, protein and microbial layers to capture regulation and emergent biology that any one omic misses.

We use joint dimensionality reduction, similarity-network fusion and machine learning on integrated features — anchored in pathways — to build robust, interpretable multi-omic models for precision medicine.

Tools & technologies

MOFA+SNFmixOmicsDIABLOscikit-learnNetworkX
Integration networkPatients/features fused across omics.
Multi-omic contrastCoordinated changes across layers.
Capabilities

What we do.

Core methods we apply in multi-omics integration.

Joint factor models

MOFA-style latent factors across omic layers.

Network fusion

Similarity-network fusion of patient profiles.

Integrated ML

Models that learn from combined multi-omic features.

Pathway integration

Mapping multi-omic signals onto shared pathways.

Microbiome multi-omics

Metagenomic + host data with machine learning.

Biomarker panels

Cross-omic signatures with clinical relevance.

Workflow

From data to insight.

How a multi-omics integration project flows end to end.

01

Omic layers

DNA · RNA · protein · microbiome

02

Harmonise

QC & scaling

03

Integrate

factors / fusion

04

Model

ML on joint features

05

Pathways

biological grounding

06

Decide

precision-medicine output

Visual analytics

Publication-grade figures.

Interactive, live-rendered visualisations used in multi-omics integration.

Integration networkPatients/features fused across omics.
Multi-omic contrastCoordinated changes across layers.
Joint embeddingSamples in integrated latent space.
Cross-omic linksConnections between genomic and other layers.
Focus

Where we go deep.

Microbiome & host

Integrated multi-omics with machine learning for precision medicine.

Systems biology

Regulation emerging from layered data.

Multi-omic biomarkers

Panels that outperform single-omic markers.

Insights

Questions we answer.

A few of the things people ask about multi-omics integration — and our short answers. Ask CGB-AI for more.

Why integrate omics?

Regulation crosses layers — a gene variant, its expression and protein output together explain phenotypes that one layer cannot.

How do you avoid noise?

Joint factor models and pathway grounding extract shared, biologically meaningful signal rather than layer-specific noise.

Selected research

Publications in Multi-Omics Integration.

Drawn from our full record of 173 papers, filtered to this area.

Browse all publications →

Start a multi-omics integration project.

Tell us the biological question and the data you have — we will map out an approach.

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