Machine learning for omics

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

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