ArticleBriefings in bioinformatics2022
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed.
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- A feature selection-based oblique hyperplane for oblique random survival forests.BMC medical research methodology · 2026Article
- The emerging role of machine learning-based methods in cancer classification using microRNA.Biochemistry and biophysics reports · 2026Review
- Optimizing feature selection in cancer microarray data using a heap-driven evolutionary framework for high-dimensional spaces.Scientific reports · 2026Article
- Explainable machine learning integrating biochemical and metabolomic biomarkers with conventional clinical factors improves chronic kidney disease prediction and risk stratification.BMC nephrology · 2026Article
- Development and validation of a long-term survival prediction model for older adults with asthma.Archives of public health = Archives belges de sante publique · 2026Article
- Benchmark of biomarker identification and prognostic modeling methods on diverse censored data.PloS one · 2026Article
- Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach.Frontiers in medicine · 2026Article
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Combined machine learning identification and experimental validation of NDUFS8 and SUCLG1 as potential biomarkers for mechanical asphyxia.Frontiers in medicine · 2026Article
- Feature learning augmented with sampling and heuristics (FLASH) improves model performance and biomarker identification.NPJ systems biology and applications · 2025Article
- Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning.World journal of gastroenterology · 2025Article
- An Interpretable Deep Learning Framework for Biomarker Discovery in Complex Disease Survival Outcomes.bioRxiv : the preprint server for biology · 2025Article
- Machine learning-based screening of potential diagnostic markers for prostate cancer with immune cell infiltration analysis and experimental validation.Translational andrology and urology · 2025Article
- Revealing gait as a murine biomarker of injury, disease, and age with multivariate statistics and machine learning.Scientific reports · 2025Article
- Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- A Hybrid Ensemble Equilibrium Optimizer Gene Selection Algorithm for Microarray Data.Biomimetics (Basel, Switzerland) · 2025Article
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4 authors.
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Abstract
Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters.
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