Ciclops

Ciclops constructs and refines explainable clinical outcome prediction models across heterogeneous cross-platform datasets using transfer learning and SHAP-based interpretability.

Key Features:

• Cross-Platform Data Integration: Harmonizes and integrates datasets from multiple measurement platforms to address heterogeneity in clinical data.
• Explainable Models: Produces interpretable prediction models that emphasize explanation of feature contributions to clinical outcomes.
• Transfer Learning Capabilities: Employs transfer learning to adapt models trained on one dataset for validation or application on other datasets, reducing the need for extensive re-training.
• SHAP Analysis Integration: Incorporates SHAP (SHapley Additive exPlanations) for post-training feature attribution and assessment of potential biomarkers.

Scientific Applications:

• Precision Medicine: Enables construction of reliable and interpretable prediction models from diverse patient data to support tailoring treatments to individuals.
• Biomarker Identification: Identifies and assesses potential biomarkers associated with clinical outcomes via SHAP-based feature attribution.

Methodology:

Harmonizing cross-platform datasets for model training, training and validating predictive models with transfer learning across datasets, and applying SHAP analysis post-training to attribute feature contributions.