ArticleBioinformatics (Oxford, England)2020
Consensus features nested cross-validation.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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Who cites it
110 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Automating Diagnosis of Skin Neglected Tropical Diseases via Patient Metadata through Machine Learning Model with Adaptive Balancing and Dual Cross-Validation: Retrospective Diagnostic Accuracy Study.Online journal of public health informatics · 2026Article
- A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Tumor-educated platelets in cancer diagnostics and prognostics: A critical appraisal and roadmap for clinical translation.International journal of cancer · 2026Review
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- Factors associated with delayed diagnosis of fibrotic interstitial lung disease: a retrospective cohort study.BMC pulmonary medicine · 2026Article
- Refining multiple artificial intelligence strategies for automatic pain assessment investigations (RUGGI Study): A study protocol.European journal of anaesthesiology and intensive care · 2026Article
- Macroscale Gradient-Informed Neural Oscillation Topography in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Patients With Sepsis: Case Study.JMIR medical informatics · 2026Article
- Machine Learning-Based Prediction of Drug-Induced QTc Changes in a Large Finnish Biobank Cohort.Clinical and translational science · 2026Article
- Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers.NPJ digital medicine · 2026Article
- Machine learning prediction and calibration of cellulose-based solid-phase extraction performance for pharmaceuticals across aqueous matrices.RSC advances · 2026Review
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Understanding and applying machine learning in differentiating abdominal IgA vasculitis without purpura from appendicitis.Pediatric research · 2026Article
- Integration of clinical and proteomic risk factors enhances prognostic modelling of incident vascular complications in type 2 diabetes.Cardiovascular diabetology · 2026Article
- Stratifying high-risk prediabetes clusters using blood-based epigenetic markers.Biomarker research · 2026Article
- Ten key considerations for developing interpretable risk prediction models.Frontiers in bioinformatics · 2026Article
50 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
summaryFeature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets.We develop consensus nested cross-validation (cnCV) that combines the idea of feature stability from differential privacy with nCV. Feature selection is applied in each inner fold and the consensus of top features across folds is used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative cooling (pEC). We also compare these methods using real RNA-seq data from a study of major depressive disorder.The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/insilico/cncv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.