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 https://gitlab.inria.fr/compo/roofs, 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, and true positive and false positive rates assessed on semi-synthetic data with a simulated outcome. We demonstrate the utility of ROOFS on data from the PIONeeR clinical trial, aimed at identifying predictors of resistance to anti-PD-(L)1 immunotherapy in lung cancer. Of the 34 FS methods gathered in ROOFS, we evaluated 23 in combination with 11 classifiers (253 models) and identified a filter based on the union of Benjamini-Hochberg false discovery rate-adjusted p-values from t-test and logistic regression as the optimal approach, outperforming other methods including widely used LASSO. We conclude that comprehensive benchmarking with ROOFS has the potential to improve the reproducibility of FS discoveries and increase the translational value of clinical models.
Machine Learning Driven Biomarker Selection for Medical Diagnosis
Fulton Schools of Engineering, Arizona State University（亚利桑那州立大学富尔顿工程学院）；Biodesign Center for Personalized Diagnostics, Arizona State University（亚利桑那州立大学生物设计个性化诊断中心）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）
Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway（癌症遗传学系，癌症研究所，奥斯陆大学医院，奥斯陆，挪威）；Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, University of Oslo, Oslo, Norway（奥斯陆生物统计与流行病学中心（OCBE），生物统计学系，奥斯陆大学，奥斯陆，挪威）；Department of Informatics, University of Oslo, Oslo, Norway（信息学系，奥斯陆大学，奥斯陆，挪威）；Department of Statistics, Ludwig Maximilian University of Munich, Munich, Germany（统计学系，慕尼黑路德维希-马克西米利安大学，慕尼黑，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））
Automated Detection of Persistent Inflammatory Biomarkers in Post-COVID-19 Patients Using Machine Learning Techniques
Era University（埃拉大学）；Era's Lucknow Medical College and Hospital（埃拉克克瑙医学院及医院）；College of Medicine, Kufa University（库法大学医学院）；University of Al-Qadisiyah（卡迪西亚大学）；Liverpool John Moors University（利物浦约翰摩尔大学）
A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment
Universiti Teknologi PETRONAS（国油科技大学）；Hospital Universiti Sains Malaysia（马来西亚理科大学医院）；Universiti Sains Malaysia（马来西亚理科大学）；Ulster University（阿尔斯特大学）；University of Calgary（卡尔加里大学）；University of Bath（巴斯大学）
Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Department of Neurosurgery, King’s College Hospital, London, UK（伦敦国王学院医院神经外科部门）；Department of Neuroradiology, Beijing Tiantan Hospital, Beijing, China（北京天坛医院神经放射科部门）；Department of Neuroradiology, King’s College Hospital, London, UK（伦敦国王学院医院神经放射科部门）
You Only Train Once: Differentiable Subset Selection for Omics Data
Department of Computer Science, ETH Zurich（计算机科学系，苏黎世联邦理工学院）；Department of Intensive Care and Neonatology, University Children’s Hospital Zurich（重症医学与新生儿科，苏黎世大学儿童医院）
A Graph Based Raman Spectral Processing Technique for Exosome Classification
Insight Centre for Data Analytics（数据分析洞察中心）；Dublin City University（都柏林城市大学）；School of Computing（计算学院）；School of Chemical Sciences（化学科学学院）；Ho Chi Minh City Open University（胡志明市开放大学）
Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Robust Ultra-High-Dimensional Variable Selection With Correlated Structure Using Group Testing
University of Maryland Baltimore Marlene & Stewart Greenebaum Comprehensive Cancer Center（马里兰大学巴尔的摩分校马伦和斯蒂文·格林堡综合癌症中心）；Case Western Reserve University（凯斯西储大学）；University of Florida（佛罗里达大学）
RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection
Department of Computer Science, New Jersey Institute of Technology（计算机科学系，新泽西理工学院）；Department of Data Science, New Jersey Institute of Technology（数据科学系，新泽西理工学院）；Department of Biostatistics（生物统计学系）；Division of Cardiology, Brown University Health（心脏病学部，布朗大学健康中心）；Department of Medicine, Warren Alpert Medical School of Brown University（医学系，布朗大学沃伦·阿尔珀特医学院）
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery
Tulane Center for Biomedical Informatics and Genomics（杜兰大学生物医学信息学与基因组学中心）；Division of Biomedical Informatics and Genomics（生物医学信息学与基因组学部门）；Deming Department of Medicine（戴明医学系）；Tulane University（杜兰大学）
Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation
Istituto Nazionale di Fisica Nucleare（意大利国家核物理研究所）；Università degli Studi di Bari（巴里大学）；INAF - Astronomical Observatory of Capodimonte（意大利国家天体物理研究所-卡波迪蒙特天文台）；Università degli Studi di Genova（热那亚大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里克二世大学）；Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR（意大利国家研究委员会智能系统与自动化研究所）
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
University of Pennsylvania（宾夕法尼亚大学）；Stony Brook University（石溪大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Medical University of South Carolina（南卡罗来纳医科大学）；Cedars-Sinai Medical Center（西格拉姆山医疗中心）
