Predictive Modelling & Precision Health Support
We develop and evaluate models that integrate genomic, phenotypic and clinical datasets for research, feasibility studies and product prototyping
Who we support
Clinical Research & Precision-Health Programs
Integrate clinical and omics data to drive research, cohort stratification, model evaluation, and tool prototyping
Biotech & Pharma
Support biomarker, mechanism, and target discovery through reproducible omics analysis, data integration, and modelling.
Digital Health Startups
Turn health data into useful models, dashboards and digital tools
Research Teams
Analyse complex data with reproducible workflows and publication-ready outputs.
What we build?
We develop and evaluate evidence-informed models integrating genomic, phenotypic, clinical and lifestyle data. Depending on the question and available data, these may support risk modelling, biomarker and mechanism research, response modelling and other precision-health applications.
Typical projects
- Identification and interpretation of genetic variants or biomarkers
- Mechanism and target investigation
- Cohort stratification and risk modelling, including PRS
- Integration of clinical and omics data
- Integration of wearable/lifestyle data with clinical and omics data
Practical outputs
- Candidate biomarkers and gene signatures
- Evidence-informed models and risk scores including polygenic risk scores
- Integrated results across clinical and omics datasets
- Reproducible analysis workflows
- Prototype dashboards and visualisations
- Clear reports, figure and technical documentation
How it’s delivered
We support projects from feasibility through prototype development, evaluation and handoff. Depending on the project, outputs may include reports, reproducible workflows, dashboards or APIs
Step 1 —Feasibility
Define the scientific question, intended use, available data, constraints and success criteria. Assess data readiness and identify the most appropriate analytical approach
Step 2 — Prototype
Develop and evaluate the prototype using transparent, evidence-informed methods, with interpretable outputs, performance assessment and clear documentation.
Step 3 — Validation & Handoff
Validation on appropriate datasets, refine the workflow and prepare outputs for use by your team. Delivery may include reproducible pipelines, reports, dashboards, APIs and technical documentation.