Genetic Diagnosis of Prostate Cancer

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to make an automated —yet totally transparent— medical diagnosis of prostate cancer from genetic sequencing of prostate tissue?

Goals & benefits

Identify the genes involved in cancer and enhance medical knowledge by helping urologists and oncologists understand the causal relationships between specific genes, their combination, and the presence of cancer.

Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses.

Contribute to improving patient care (pain, survival, duration of treatment) and extend access to high-level diagnoses even in medical deserts.


We get a Predictive Model that is:


A Decision System composed of 4 unchained gradual rules, each using some of the 7 variables that XTRACTIS identified as significant, including weak signals (out of the 12,600 Potential Predictors are the level of expression of genes characterizing each patient).


Excellent Real Performance on External Test.

Efficient & Operational.

Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

 Use Case 10/2022 (v1.6)

Results by
XTRACTIS® GENERATE 11.2.38531 (06/2021)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 3 cases
  5. Top-Models Benchmark