machine-learning-for-omicslisted

# Machine Learning For Omics ## Version Compatibility Reference examples assume recent stable releases of the preferred tools, especially `scikit-learn` and the other tools listed below. Before using code or command patterns, verify installed versions match the environment: - Python: `python -c "import <module>; print(<module>.__version__)"` - CLI: `<tool> --version` - If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged. ## Overview Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features. ## When To Use This Skill - use when the task is supervised learning on omics features - use when the user needs a model, validation metrics, and interpretable feature importance - use when the modeling objective is biomarker discovery, classification, regression, or survival prediction ## Quick Route - If the input is raw or minimally processed data, start with validation and QC before any modeling. - If the input is already processed, skip directly to the first workflow step that matches the user goal. - If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result. ## Progressive Disclosure - Read `references/technical_reference.md` when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance. - Keep `SKILL.md` as the main execution path and load the r