ArticleComputational and structural biotechnology journal2023
Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Machine learning versus traditional regression models for predicting diabetic retinopathy screening adherence among community-dwelling older adults with diabetes.Frontiers in endocrinology · 2026Article
- Prediction of Esterification and Antioxidant Properties of Food-Derived Fatty Acids and Ascorbic Acid Based on Machine Learning: A Review.Foods (Basel, Switzerland) · 2025Review
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- Machine learning-based prediction models for renal impairment in Chinese adults with hyperuricaemia: risk factor analysis.Scientific reports · 2025Article
- Integrative bioinformatics approaches reveal key hub genes in cyanobacteria: insights from Synechocystis sp. PCC 6803 and Geminocystis sp. NIES-3708 under abiotic stress conditions.Genes & genomics · 2025Article
- Identification of key genes in periodontitis.Frontiers in genetics · 2025Article
- Data-driven platform for identifying variants of interest in COVID-19 virus.Computational and structural biotechnology journal · 2022Article
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8 authors.
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Abstract
Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 10
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