BRISK: Rank-Based Bayesian Feature Selection for High-Dimensional Data via Robust Permutation Kernel

High-dimensional biomedical data pose challenges such as multicollinearity, small sample sizes, and instability in variable selection. Feature selection is crucial for interpretability, reproducibility, and robust statistical learning. Traditional Bayesian methods, such as SSVS and spike-and-slab priors, often depend on precise distributional assumptions and are sensitive to prior choices, limiting their stability and transparency in prioritizing variables. We propose BRISK, a rank-based Bayesian feature selection framework utilizing robust permutation kernels, which unifies feature-specific evidence into stable rankings based on association strength and consistency across modeling scenarios. Unlike binary selection, BRISK generates a prioritized list, aiding validation and expert review. Empirical results on simulated and real omics data demonstrate BRISK's superior stability, reduced false discoveries, and improved predictive accuracy compared to traditional methods, especially when predictors are correlated or samples are limited. BRISK offers a reliable, interpretable approach for high-dimensional biomedical feature selection.

Feature selection

High-dimensional data

Rank-based framework

Bayesian methods

Stability

Omics data application 

International Society for Bayesian Analysis (ISBA)