Audit data and labels; define targets, metrics, and leakage checks

Engineered features from multi-omics/metadata

Trained baselines and advanced models with cross-validation and hyperparameter tuning

Report calibrated performance (ROC/PR, confusion matrices) and uncertainty

Explainability (feature importance) and sensitivity analyses

Packaged the model for use (Docker/Conda, inference notebook or API stub)

A clear methods note and recommendations for deployment or next experiments