Biomarker Discovery

Feature Selection

LASSO Regression

Stability Selection

from sklearn.linear_model import LassoCV

class BiomarkerSelector:
    def stability_selection(self, X, y, threshold=0.8, n_bootstrap=100):
        selection_counts = np.zeros(X.shape[1])
        for _ in range(n_bootstrap):
            idx = np.random.choice(len(X), size=len(X), replace=True)
            lasso = LassoCV(cv=3, random_state=42)
            lasso.fit(X[idx], y[idx])
            selection_counts[np.where(lasso.coef_ != 0)[0]] += 1
        return np.where(selection_counts / n_bootstrap > threshold)[0]

Multi-Omics Integration

Canonical Correlation Analysis

class MultiOmicsIntegrator:
    def mofa_integration(self, n_factors=10):
        from sklearn.decomposition import FactorAnalysis
        concatenated = np.hstack([v['data'] * v['weights'] for v in self.views.values()])
        return FactorAnalysis(n_components=n_factors).fit_transform(concatenated)

Proteomics ML