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bio-machine-learning-omics-classifiers

Maintainer FreedomIntelligence · Last updated April 1, 2026

Train classifiers on omics data: random forest, SVM, XGBoost.

OpenClawNanoClawAnalysisReproductionbio-machine-learning-omics-classifiers🧠 bioos extended suitebioos extended bioinformatics suitetrain

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-machine-learning-omics-classifiers

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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).
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).

Source Doc

Excerpt From SKILL.md

XGBoost Classifier

from xgboost import XGBClassifier

## Logistic Regression with Regularization

```python
from sklearn.linear_model import LogisticRegressionCV

## Get selected features (nonzero coefficients)

feature_mask = logit.coef_[0] != 0
selected = X.columns[feature_mask]

Use cases

Use bio-machine-learning-omics-classifiers for genomics and bioinformatics workflows.
Apply bio-machine-learning-omics-classifiers to sequencing, variant, or omics analysis tasks.