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[2601.05151] ROOFS: RObust biOmarker Feature Selection
Summary
The paper introduces ROOFS, a Python package for robust biomarker feature selection, addressing challenges in biomedical data through comprehensive benchmarking of various methods.
Why It Matters
ROOFS aims to bridge the gap between advanced feature selection methodologies and their practical application in biomedical research. By providing a systematic approach to feature selection, it enhances the reproducibility and translational value of clinical models, which is crucial for improving patient outcomes in precision medicine.
Key Takeaways
- ROOFS benchmarks multiple feature selection methods tailored to user data.
- The package generates reports summarizing evaluation metrics for better decision-making.
- It demonstrated improved predictive performance in identifying resistance to immunotherapy in lung cancer.
- Comprehensive benchmarking can enhance the reproducibility of feature selection discoveries.
- ROOFS outperformed widely used methods like LASSO in specific applications.
Statistics > Machine Learning arXiv:2601.05151 (stat) [Submitted on 8 Jan 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:ROOFS: RObust biOmarker Feature Selection Authors:Anastasiia Bakhmach, Paul Dufossé, Andrea Vaglio, Florence Monville, Laurent Greillier, Fabrice Barlési, Sébastien Benzekry View a PDF of the paper titled ROOFS: RObust biOmarker Feature Selection, by Anastasiia Bakhmach and 6 other authors View PDF HTML (experimental) Abstract:Feature selection (FS) is essential for biomarker discovery and clinical predictive modeling. Over the past decades, methodological literature on FS has become rich and mature, offering a wide spectrum of algorithmic approaches. However, much of this methodological progress has not fully translated into applied biomedical research. Moreover, challenges inherent in biomedical data, such as high-dimensional feature space, low sample size, multicollinearity, and missing values, make FS non-trivial. To help bridge this gap between methodological development and practical application, we propose ROOFS (RObust biOmarker Feature Selection), a Python package available at this https URL, designed to help researchers in the choice of FS method adapted to their problem. ROOFS benchmarks multiple FS methods on the user's data and generates reports summarizing a comprehensive set of evaluation metrics, including downstream predictive performance estimated using optimism correction, stability, robustness of individual features, an...
