bio-machine-learning-omics-classifiers
Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill omics-classifiers --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: matplotlib 3.8+, pandas 2.2+, scikit-learn 1.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Classification Models for Omics Data **"Build a classifier from my gene expression data"** → Train RandomForest, XGBoost, or logistic regression models on omics features with proper preprocessing and evaluation metrics. - Python: `sklearn.ensemble.RandomForestClassifier()`, `xgboost.XGBClassifier()` ## Core Workflow **Goal:** Train a classification model on omics data and evaluate its predictive performance. **Approach:** Build a scaled pipeline with a Random Forest classifier, fit on training data, and assess with ROC-AUC on held-out test data. ```python from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestClassifier from sklearn.metric
- Version Compatibility
- Core Workflow
- XGBoost Classifier
- Logistic Regression with Regularization
- ROC Curve Visualization
- Multi-class Classification
- Feature Importance from Trees
- Preprocessing Guidelines
- Related Skills
What does the bio-machine-learning-omics-classifiers skill do?
Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill omics-classifiers --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.