Publications
Peer-reviewed evidence
Publications and manuscripts that support EvoClin’s scientific foundation, including cancer evolution, molecular modelling, clinical endpoints and disease-specific validation.
Science
Evidence-trained AI for molecular oncology.
EvoClin combines international guidelines, peer-reviewed publications, curated molecular cohorts and evolution-informed models to support NGS prescription, appropriateness assessment, molecular interpretation and outcome prediction.
International guidelines
Peer-reviewed publications
Molecular patient cohorts
Outcome prediction
Explore the science
EvoClin’s scientific layer connects evidence-based oncology knowledge with patient-level molecular and clinical data.
Methods
AI, guidelines and evolution
The methodological layer combines cohort-trained machine learning, curated guideline logic and evolution-derived features to assist molecular decisions.
Datasets & collaborations
Cohorts and clinical partners
Molecular and clinical datasets are used to train, test and validate models for risk stratification, disease trajectories and clinically meaningful outcomes.
A science layer built for clinical use
The same scientific architecture supports physicians and molecular biologists before, during and after molecular testing.
Prescription and appropriateness
EvoClin uses guideline-aware and literature-aware AI to help evaluate which molecular tests are appropriate for a clinical context and when repeating NGS or ctDNA is likely to be useful.
From report to clinical meaning
Molecular findings are interpreted alongside clinical variables, longitudinal history and published evidence so that complex results become readable for clinical decision making.
Risk, relapse and resistance
Cohort-trained and evolution-informed models support prediction of clinically relevant outcomes, including prognosis, relapse, disease-free survival, metastasis and therapy resistance.