ArticleScientific reports2023
Stability selection for LASSO with weights based on AUC.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Plasma Glycome Differences Between Women with PCOS and Healthy Controls.International journal of molecular sciences · 2026Article
- Comment on "Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon".Hepatology international · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Development and Internal Validation of a LASSO-Based Prediction Model for Colorectal Adenoma Recurrence After Polypectomy.Cancer management and research · 2026Article
- Fire susceptibility assessment in the Carpathians using an interpretable framework.Scientific reports · 2025Article
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- Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data.PloS one · 2025Article
- Molecular Landscape of Bladder Cancer: Key Genes, Transcription Factors, and Drug Interactions.International journal of molecular sciences · 2024Article
- Unveiling the Mechanisms Underlying the Immunotherapeutic Potential of Gene-miRNA and Drugs in Head and Neck Cancer.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to optimize cancEr tReatment (G-DEFINER): study protocol of an observational prospective multicenter study.Acta oncologica (Stockholm, Sweden) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stability selection is a variable selection algorithm based on resampling a dataset. Based on stability selection, we propose weighted stability selection to select variables by weighing them using the area under the receiver operating characteristic curve (AUC) from additional modelling. Through an extensive simulation study, we evaluated the performance of the proposed method in terms of the true positive rate (TPR), positive predictive value (PPV), and stability of variable selection. We also assessed the predictive ability of the method using a validation set. The proposed method performed similarly to stability selection in terms of the TPR, PPV, and stability. The AUC of the model fitted on the validation set with the selected variables of the proposed method was consistently higher in specific scenarios. Moreover, when applied to radiomics and speech signal datasets, the proposed method had a higher AUC with fewer variables selected. A major advantage of the proposed method is that it enables researchers to select variables intuitively using relatively simple parameter settings.
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