Connect molecular and clinical context
Combine detected mutations, copy-number alterations, cytogenetics, blood counts, patient history and therapeutic variables.
EvoClin includes machine-learning frameworks trained on large patient cohorts and molecular profiles to transform routine data into interpretable risk, trajectory and outcome signals.
The scientific engine is designed to extract usable signals from complex oncology data while keeping outputs readable and biologically grounded.
Combine detected mutations, copy-number alterations, cytogenetics, blood counts, patient history and therapeutic variables.
Train and validate models on thousands of patients with curated molecular and clinical data across disease-specific cohorts.
Move beyond static mutation lists by modeling co-occurrence, temporal order and evolution-derived features where validated.
Translate model outputs into interpretable risk categories, outcome estimates, trajectories and molecular decision signals.
ProgEvo, ProgMet and PRECISE are designed as disease-aware machine-learning frameworks that transform routine molecular and clinical data into transparent, testable oncology models.
EvoClin separates the scientific model layer from the hospital product layer, so models can be validated rigorously and then exposed through simple clinical interfaces.
Clean and harmonize molecular, biological, clinical and therapeutic data.
Train disease-aware AI frameworks on large patient cohorts.
Evaluate risk, trajectory and outcome signals in independent data.
Expose validated outputs in tools, hospital workflows and partner projects.
EvoClin frameworks are currently intended for research and educational purposes. They are not certified medical devices and should not be used as stand-alone tools for clinical decision making.