README.md
# Machine Learning For Omics
## Scope
Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features.
## Typical Inputs
- feature matrix
- labels or outcomes
- split or validation design
## Typical Outputs
- trained model
- validation metrics
- feature importance or explanation summaries
## Conceptual Seeds
- omics machine-learning workflows
- scientific modeling and explainability patterns
## Optional Supplements
- `scikit-learn`
- `statsmodels`
## Main Skill File
- `SKILL.md`
references/technical_reference.md
# Machine Learning For Omics Technical Reference
## Purpose
This reference file provides deeper implementation notes for `machine-learning-for-omics`.
## When To Read This File
- 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
## Detailed Inputs
- feature matrix
- labels or outcomes
- split or validation design
## Detailed Outputs
- trained model
- validation metrics
- feature importance or explanation summaries
## Tooling Notes
- scikit-learn
- statsmodels
- survival tooling where needed
- shap when appropriate
## Detailed Workflow Notes
### 1. Define the prediction task
Clarify outcome type, class balance, leakage risks, and validation plan.
### 2. Build a reproducible split
Use train-validation-test or cross-validation schemes that respect cohort structure.
### 3. Train parsimonious models first
Start with robust baseline models before complex architectures.
### 4. Evaluate honestly
Report calibration, held-out performance, and failure modes instead of only one metric.
### 5. Explain cautiously
Use importance or explanation methods as interpretation aids, not proof of causality.
## Validation Priorities
- Confirm identifiers and metadata join correctly before modeling or summarizing.
- Generate at least one QC artifact before final biological interpretation.
- Keep raw or minimally processed inputs separate from transformed outputs.
- Verify that modalities, samples, and model assumptions align before integration or inference.
- Export factors, scores, or model outputs together with interpretation context.
## Common Failure Modes
- leakage across train and test sets
- high-dimensional modeling without strong regularization or validation
- presenting feature importance as mechanistic causality
## Optional Supplements
- `scikit-learn`
- `statsmodels`
## Conceptual Provenance
- omics machine-learning workflows
- scientific modeling and explainability patterns
SKILL.md
---
name: machine-learning-for-omics
description: Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features.
tool_type: python
primary_tool: scikit-learn
---
# 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 reference file only when the task or failure mode needs the extra detail.
## Default Rules
- Prefer Python-first workflows unless the task explicitly requires something else.
- Keep intermediate and final outputs separated.
- Record software versions, reference builds, and key parameters when they affect interpretation.
- Favor reproducible tables and figures over one-off interactive-only outputs.
## Expected Inputs
- feature matrix
- labels or outcomes
- split or validation design
## Expected Outputs
- trained model
- validation metrics
- feature importance or explanation summaries
## Preferred Tools
- scikit-learn
- statsmodels
- survival tooling where needed
- shap when appropriate
## Starter Pattern
```text
Preferred starting point: scikit-learn
Inputs: feature matrix, labels or outcomes, split or validation design
Outputs: trained model, validation metrics, feature importance or explanation summaries
```
## Workflow
### 1. Define the prediction task
Clarify outcome type, class balance, leakage risks, and validation plan.
### 2. Build a reproducible split
Use train-validation-test or cross-validation schemes that respect cohort structure.
### 3. Train parsimonious models first
Start with robust baseline models before complex architectures.
### 4. Evaluate honestly
Report calibration, held-out performance, and failure modes instead of only one metric.
### 5. Explain cautiously
Use importance or explanation methods as interpretation aids, not proof of causality.
## Output Artifacts
- Recommended output layout:
- `results/` for final tables and serialized objects
- `figures/` for plots and static visual exports
- `qc/` for checks that justify downstream interpretation
- Minimum expected outputs for this skill:
- `trained model`
- `validation metrics`
- `feature importance or explanation summaries`
## Quality Review
- Confirm identifiers and metadata join correctly before modeling or summarizing.
- Generate at least one QC artifact before final biological interpretation.
- Keep raw or minimally processed inputs separate from transformed outputs.
- Verify that modalities, samples, and model assumptions align before integration or inference.
- Export factors, scores, or model outputs together with interpretation context.
## Anti-Patterns
- leakage across train and test sets
- high-dimensional modeling without strong regularization or validation
- presenting feature importance as mechanistic causality
## Related Skills
- `Multi-Omics Integration`
- `Pathway Analysis`
- `Systems Biology`
- `Causal Genomics`
## Optional Supplements
- `scikit-learn`
- `statsmodels`