Computational Medicine
Computational medicine translates molecular, cellular, and clinical data into predictive models of disease progression. We combine medical knowledge graphs with computer vision for digital pathology and polygenic risk scoring. These models personalize therapeutic interventions, tailoring care to each patient's risk profile.
What this area is.
Computational medicine turns molecular and clinical data into predictions that change decisions: who is at risk, what a tumour will do next, which therapy fits which patient.
We combine predictive analytics, computational pathology and knowledge graphs with explainable AI, keeping clinicians in the loop and models interpretable.
Tools & technologies
What we do.
Core methods we apply in computational medicine.
Disease-risk prediction
Polygenic and multi-omic models estimating individual risk.
Predictive analytics & outcomes
Forecasting progression, response and outcome.
Clinical decision-support systems
Interpretable recommendations grounded in evidence.
Medical knowledge graphs
Linking genes, drugs, diseases and phenotypes for reasoning.
Computational pathology
Deep learning over histology and digital pathology.
Biomedical foundation models
Large models adapted to clinical and molecular data.
From data to insight.
Data
molecular · clinical · imaging
Model
ML · foundation models
Predict
risk · outcome · diagnosis
Validate
external cohorts
Explain
attributions & evidence
Clinic
decision support
Publication-grade figures.
Where we go deep.
Precision therapeutics
Matching patients to the therapy most likely to work.
Virtual clinical trials
In-silico modelling to design and de-risk trials.
AI-assisted diagnosis
Decision support that augments clinical judgement.
Questions we answer.
A few common questions about computational medicine. Ask CGB-AI for more.
What is computational medicine?
The discipline of predicting and personalizing care with computational models — from disease risk to diagnosis to therapy selection.
Does AI replace the clinician?
No — models predict and prioritize; clinicians interpret and decide. We keep humans in the loop and predictions explainable.
Publications in Computational Medicine.
Drawn from our record of 170 papers, filtered to this area.
Start a computational medicine project.
Tell us the clinical question and the data you have — we will map out an approach.
