What the technology does

The scientific engine is designed to extract usable signals from complex oncology data while keeping outputs readable and biologically grounded.

Data integration

Connect molecular and clinical context

Combine detected mutations, copy-number alterations, cytogenetics, blood counts, patient history and therapeutic variables.

Model training

Learn from large patient cohorts

Train and validate models on thousands of patients with curated molecular and clinical data across disease-specific cohorts.

Evolution

Reconstruct trajectory-informed signals

Move beyond static mutation lists by modeling co-occurrence, temporal order and evolution-derived features where validated.

Clinical outputs

Return decision-ready summaries

Translate model outputs into interpretable risk categories, outcome estimates, trajectories and molecular decision signals.

Scientific engine

Evolution-informed models behind EvoClin

ProgEvo, ProgMet and PRECISE are designed as disease-aware machine-learning frameworks that transform routine molecular and clinical data into transparent, testable oncology models.

From model to clinical workflow

EvoClin separates the scientific model layer from the hospital product layer, so models can be validated rigorously and then exposed through simple clinical interfaces.

01

Curate

Clean and harmonize molecular, biological, clinical and therapeutic data.

02

Model

Train disease-aware AI frameworks on large patient cohorts.

03

Validate

Evaluate risk, trajectory and outcome signals in independent data.

04

Deploy

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.