ArticleBioinformatics (Oxford, England)2018
The revival of the Gini importance?
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 213 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
213 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainable artificial intelligence for spectroscopy data: a review.Pflugers Archiv : European journal of physiology · 2025Pooled it
- A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.Computational biology and chemistry · 2022Pooled it
- Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis.Molecules (Basel, Switzerland) · 2020Pooled it
- Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.BMC psychiatry · 2024Trial
- Added Value of One-Month Clinical Data in Predicting Chronic-Stage Motor Function After Ischemic Stroke.Journal of clinical medicine · 2026Article
- Determinants of mutation susceptibility along the genome are largely invariant across human tissues.Genome biology · 2026Article
- SARS-CoV-2 Infection-Induced Alterations in ADAR Editing Patterns Differ Between Patients Who Developed Critical Compared to Non-Critical COVID-19.International journal of molecular sciences · 2026Article
- Machine learning identifies neutrophil-related signatures for diagnostic value in neonatal sepsis.Journal of clinical biochemistry and nutrition · 2026Article
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Persistent white matter disruption underlies apparent functional normalization in intractable temporal lobe epilepsy: Evidence from multimodal MRI.IBRO neuroscience reports · 2026Article
- Improvements in substance use and social determinants of health associated with hepatitis C treatment initiation and sustained virologic response in opioid treatment programs.Addictive behaviors reports · 2026Article
- Article
- Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.JMIR formative research · 2026Article
- On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data.bioRxiv : the preprint server for biology · 2026Article
- Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.Chemical research in toxicology · 2026Article
- Comparative evaluation of feature selection methods for HRV-based survival modeling in HIV-positive ICU patients: a retrospective study.BMC medical informatics and decision making · 2026Article
- PETIL: Predicting Expansion of Tumor Infiltrating Lymphocytes for the Adoptive Cell Immunotherapy in Bladder Cancers.bioRxiv : the preprint server for biology · 2026Article
- Sociodemographic Risk Factors for Incomplete Routine Childhood Immunizations Among Children Born During the COVID-19 Pandemic, Louisiana, March 1, 2020-July 1, 2021.Public health reports (Washington, D.C. : 1974) · 2026Article
- A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain.PLoS computational biology · 2026Article
- From gaze to proficiency: deep learning-driven prediction of novice performance in laparoscopic training using AOI-dependent metrics.Surgical endoscopy · 2026Article
153 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Random forests are fast, flexible and represent a robust approach to analyze high dimensional data. A key advantage over alternative machine learning algorithms are variable importance measures, which can be used to identify relevant features or perform variable selection. Measures based on the impurity reduction of splits, such as the Gini importance, are popular because they are simple and fast to compute. However, they are biased in favor of variables with many possible split points and high minor allele frequency. Results: We set up a fast approach to debias impurity-based variable importance measures for classification, regression and survival forests. We show that it creates a variable importance measure which is unbiased with regard to the number of categories and minor allele frequency and almost as fast as the standard impurity importance. As a result, it is now possible to compute reliable importance estimates without the extra computing cost of permutations. Further, we combine the importance measure with a fast testing procedure, producing p-values for variable importance with almost no computational overhead to the creation of the random forest. Applications to gene expression and genome-wide association data show that the proposed method is powerful and computationally efficient. Availability and implementation: The procedure is included in the ranger package, available at https://cran.r-project.org/package=ranger and https://github.com/imbs-hl/ranger. 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.