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 majiayu000/claude-skill-registry --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.
# Classification Models for Omics Data ## Core Workflow ```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.metrics import classification_report, roc_auc_score, roc_curve import matplotlib.pyplot as plt X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42) pipe = Pipeline([ ('scaler', StandardScaler()), ('clf', RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)) ]) pipe.fit(X_train, y_train) y_pred = pipe.predict(X_test) y_prob = pipe.predict_proba(X_test)[:, 1] print(classification_report(y_test, y_pred)) print(f'ROC-AUC: {roc_auc_score(y_test, y_prob):.3f}') ``` ## XGBoost Classifier ```python from xgboost import XGBClassifier # Use sklearn-compatible API with proper parameters (avoid deprecated seed, nthread) xgb = XGBClassifier( n_estimators=100, max_depth=6, learning_rate=0.1, random_state=42, # NOT seed n_jobs=-1, # NOT nthread eval_metric='logloss' ) pipe = Pipeline([('scaler', StandardScaler()), ('clf', xgb)]) pipe.fit(X_train, y_train) ``` ## Logis
- 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 majiayu000/claude-skill-registry --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 majiayu000/claude-skill-registry, a repository with 534 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.