Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/omics-classifiers
SKILL.md
Classification Models for Omics Data
Core Workflow
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
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)
Logistic Regression with Regularization
from sklearn.linear_model import LogisticRegressionCV
# L1 for sparse biomarkers, L2 for correlated features, elasticnet for mixed
logit = LogisticRegressionCV(
Cs=10,
cv=5,
penalty='l1',
solver='saga',
max_iter=1000,
random_state=42
)
pipe = Pipeline([('scaler', StandardScaler()), ('clf', logit)])
pipe.fit(X_train, y_train)
# Get selected features (nonzero coefficients)
feature_mask = logit.coef_[0] != 0
selected = X.columns[feature_mask]
ROC Curve Visualization
fpr, tpr, _ = roc_curve(y_test, y_prob)
auc = roc_auc_score(y_test, y_prob)
plt.figure(figsize=(6, 6))
plt.plot(fpr, tpr, label=f'ROC (AUC = {auc:.3f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.savefig('roc_curve.png', dpi=150)
Multi-class Classification
from sklearn.metrics import classification_report
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y_encoded = le.fit_transform(y)
# Use class_weight for imbalanced data
rf = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=42)
Feature Importance from Trees
import pandas as pd
importances = pipe.named_steps['clf'].feature_importances_
feature_imp = pd.DataFrame({'feature': X.columns, 'importance': importances})
feature_imp = feature_imp.sort_values('importance', ascending=False).head(20)
Preprocessing Guidelines
| Data Type | Scaler | Notes |
|---|---|---|
| Log-counts (RNA-seq) | StandardScaler | Assumes ~normal after log |
| TPM/FPKM | StandardScaler | Gene-wise centering |
| Raw counts | None | Tree models handle counts |
| Mixed features | ColumnTransformer | Different scalers per type |
Related Skills
- machine-learning/model-validation - Proper model evaluation
- machine-learning/prediction-explanation - Explain predictions with SHAP
- machine-learning/biomarker-discovery - Reduce features before modeling
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
majiayu000/claude-skill-registry
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
majiayu000/claude-skill-registry
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
majiayu000/claude-skill-registry
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
majiayu000/claude-skill-registry
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
majiayu000/claude-skill-registry
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
majiayu000/claude-skill-registry
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
Didn't find tool you were looking for?