Version Compatibility

Reference examples tested with: numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, boruta 0.4+, mrmr-selection 0.2+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

BorutaPy expects numpy arrays and breaks on newer numpy where the np.float/np.int aliases were removed -- pin a compatible numpy or use a maintained fork. On scikit-learn 1.8+ the LogisticRegression(penalty=) argument is deprecated (removed in 1.10) in favor of l1_ratio+C; the examples show the 1.4-1.7 form. If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Feature Selection for Biomarker Discovery

"Find the biomarkers in my omics data" -> First decide which question is being answered (all-relevant vs minimal-optimal), then select features INSIDE a resampling loop, then quantify stability -- because a selected list means little without it.

• All-relevant (which genes carry signal?): BorutaPy(rf)
• Minimal-optimal (smallest predictive set?): ElasticNetCV, LogisticRegressionCV(penalty='elasticnet')
• Stability (does the list reproduce?): bootstrap selection frequencies + a stability index

The Single Most Important Modern Insight -- Most Gene Signatures Do Not Replicate, and Significance Is the Wrong Bar

A signature being "significantly associated with outcome" is near-worthless evidence: random gene sets -- and signatures of biologically irrelevant phenomena -- are significantly associated with breast-cancer survival, often matching published prognostic signatures, because the transcriptome is dominated by a few axes (proliferation) that almost any large gene set captures (Venet 2011). The correct null is not "no association" but random gene sets of equal size plus a proliferation meta-gene. Two further hard facts complete the picture: many disjoint gene lists predict equally well (Ein-Dor 2005), so non-overlap with a prior list is the expected result, not a contradiction; and obtaining a stable list (as opposed to an accurate predictor) needs on the order of thousands of samples (Ein-Dor 2006), far more than typical omics n.