# Machine Learning For Omics > Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features. Use when this capability is needed. - Skill: `tomevault-io/machine-learning-for-omics` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/machine-learning-for-omics` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/machine-learning-for-omics/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/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 ; print(.__version__)"` - CLI: ` --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` --- > Source: [zongtingwei/Bioclaw_Skills_Hub](https://github.com/zongtingwei/Bioclaw_Skills_Hub) — distributed by [TomeVault](https://tomevault.io).